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Publication History 2003-2015

Year : [2018][2017] [2016] [2015] [2014] [2013] [2012] [2011] [2010] [2009] [2008] [2007] [2006] [2005] [2004] [2003]

2015

[Conference Paper][Journal Article]

Conference Paper

  • Y. Shen, W.-Y. Lin, J.-C. Yan, M.-L. Xu, J.-X. Wu, and J.-D. Wang. Person re-identification with correspondence structure learning. In: Proceedings of the 15th International Conference on Computer Vision (ICCV'15), Santigao, Chile, 2015.

  • Y. Yu and Q. Chao. Running time analysis: Convergence-based analysis reduces to switch analysis. In: Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC'15), Sendai, Japan, 2015, pp.2603-2610.

  • W.-Z. Dai, S. H. Muggleton, and Z.-H. Zhou. Logic vision: Meta-intepretive learning for simple geometrical concepts. In: Proceedings of the 25th International Conference on Inductive Logic Programming (ILP'15), Kyoto, Japan, 2015.

  • C. Qian, Y. Yu, and Z.-H. Zhou. Subset selection by pareto optimization. In: Advances in Neural Information Processing Systems 28 (NIPS'15) (Montreal, Canada), x. xxx, eds. Cambridge, MA: MIT Press, 2015.

  • W.-Z. Dai and Z.-H. Zhou. Statistical unfolded logic learning. In: Proceedings of the 7th Asian Conference on Machine Learning (ACML'15), Hong Kong, 2015, JMLR: W&CP 45, pp.349-361.

  • Y. Zhu, W. Gao, and Z.-H. Zhou. One-pass multi-view learning. In: Proceedings of the 7th Asian Conference on Machine Learning (ACML'15), Hong Kong, 2015, JMLR: W&CP 45, pp.407-422.

  • H.-J. Ye, D.-C. Zhan, Y. Miao, Y. Jiang, and Z.-H. Zhou. Rank consistency based multi-view learning: A privacy-preserving approach. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM'15), Melbourne, Australia, 2015, pp.991-1000.

  • J. Yi, L. Zhang, T. Yang, W. Liu, and J. Wang. An efficient semi-supervised clustering algorithm with sequential constraints. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'15), Sydney, Australia, pp.1405-1414, 2015.

  • M. Mahdavi, L. Zhang, and R. Jin. Lower and upper bounds on the generalization of stochastic exponentially concave optimization. In Proceedings of the 28th Conference on Learning Theory (COLT'15), Paris, France, 2015, pp.1305-1320.

  • M. Xu, R. Jin, and Z.-H. Zhou. CUR algorithm for partially observed matrices. In: Proceedings of the 32nd International Conference on Machine Learning (ICML'15), Lille, France, 2015, pp.1412-1421.

  • L. Luo, Y.-B. Xie, Z. Zhang, and W.-J. Li. Support matrix machines. In: Proceedings of the 32nd International Conference on Machine Learning (ICML'15), Lille, France, 2015, pp.938-947.

  • T.-B. Yang, L. Zhang, R. Jin, and S.-H. Zhu. Theory of dual-sparse regularized randomized reduction. In: Proceedings of the 32nd International Conference on Machine Learning (ICML'15), Lille, France, 2015, pp.305-314.

  • T.-B. Yang, L. Zhang, R. Jin, and S.-H. Zhu. An explicit sampling dependent spectral error bound for column subset selection. In: Proceedings of the 32nd International Conference on Machine Learning (ICML'15), Lille, France, 2015, pp.135-143.

  • Q.-Y. Jiang and W.-J. Li. Scalable graph hashing with feature transformation. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.2248-2254.

  • W. Gao and Z.-H. Zhou. On the consistency of AUC pairwise optimization. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.939-945.

  • C. Qian, Y. Yu, and Z.-H. Zhou. On constrained boolean pareto optimization. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.389-395.

  • S.-J. Huang, S. Chen, and Z.-H. Zhou. Multi-label active learning: Query type matters. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.946-952.

  • Y. Yang, H.-J. Ye, D.-C. Zhan, and Y. Jiang. Auxiliary information regularized machine for multiple modality feature learning. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.1033-1039.

  • J. Zhong, K. Tang, and Z.-H. Zhou. Active learning from crowds with unsure option. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.1061-1067.

  • L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou. A simple homotopy algorithm for compressive sensing. In: Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS'15), San Diego, CA, 2015, JMLR: W&CP 38, pp.1116-1124.

  • X.-Y. Dai, J.-B. Zhang, S.-J. Huang, J.-J. Chen, and Z.-H. Zhou. Structured sparsity with group-graph regularization. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), Austin, TX, 2015, pp.1714-1720.

  • C. Qian, Y. Yu, and Z.-H. Zhou. Pareto ensemble pruning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), Austin, TX, 2015, pp.2935-2941.

  • L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou. Online bandit learning with non-convex losses. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), Austin, TX, 2015, pp.3158-3164.



Top

Journal Article

  • H. Wang and W.-J. Li. Relational collaborative topic regression for recommender systems. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(5): 1343-1355, 2015.

  • Z. Zhao, X. He, L. Zhang, W. Ng, and Y. Zhuang. Graph regularized feature selection with data reconstruction. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2015.

  • X. He, C. Zhang, L. Zhang, and X. Li. A optimal projection for image representation. IEEE Transactions on Pattern Analysis & Machine Intelligence (TPAMI), 2015.

  • M. Lin, L. Zhang, R. Jin, S. Weng, and C. Zhang. Online kernel learning with nearly constant support vectors. Neurocomputing, 2015.

  • Z. Guan, L. Zhang, J. Peng, and J. Fan. Multi-view concept learning for data representation. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(11): 3016 - 3028 , 2015.

  • Z. Zhao, L. Zhang, X. He, and W. Ng. Expert finding for question answering via graph regularized matrix completion. IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(4): 993 - 1004, 2015.

  • Q. Qian, R. Jin, J. Yi, L. Zhang, and S. Zhu. Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (SGD). Machine Learning, 99(3): 353 - 372, 2015.

  • P. Li, J. Bu, L. Zhang, and C. Chen. Graph-based local concept coordinate factorization Knowledge and Information Systems (KAIS), 43(1): 103 - 126, 2015.

  • Z. He, C. Chen, J. Bu, C. Wang, L. Zhang, D. Cai, and X. He. Unsupervised document summarization from data reconstruction perspective Neurocomputing, 157: 356 - 366, 2015.

  • C.-L. Sun, Y.-C. Jin, J.-C. Zeng, and Y. Yu. A two-layer surrogate-assisted particle swarm optimization algorithm. Soft Computing,19(6):1461-1475, 2015.

  • S.-J. Xiao, D. Xu, and J.-X. Wu. Automatic face naming by learning discriminative affinity matrices from weakly labeled images. IEEE Transactions on Neural Networks and Learning Systems, 26(10), 2015: 2440-2452.

  • J.-X. Wu and H. Yang. Linear regression based efficient SVM learning for large scale classification. IEEE Transactions on Neural Networks and Learning Systems, 26(10), 2015: pp. 2357-2369.

  • J.-X. Wu, Y. Zhang, and W.-Y. Lin. Good practices for learning to recognize actions using FV and VLAD. IEEE Transactions on Cybernetics, accepted for publication. DOI: 10.1109/TCYB.2015.2493538.

  • Y.-F. Li and Z.-H. Zhou. Towards making unlabeled data never hurt. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 175-188.

  • Y. Yu, C. Qian, and Z.-H. Zhou. Switch analysis for running time analysis of evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 2015, 15(6): 777-792.

  • J. Liu, Y. Jiang, Z. Li, Z.-H. Zhou, and H. Lu. Partially shared latent factor learning with multiview data. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(6): 1233-1246.

  • J.-H. Hu, D.-C. Zhan, X. Wu, Y. Jiang, and Z.-H. Zhou. Pairwised specific distance learning from physical linkages. ACM Transactions on Knowledge Discovery from Data, 2015, 9(3): Article 20.

  • Q. Guo, T. Chen, Z.-H. Zhou, O. Temam, L. Li, D. Qian, and Y. Chen. Robust design space modeling. ACM Transactions on Design Automation of Electronic Systems, 2015, 20(2): Article 18.

  • W. Wang and Z.-H. Zhou. Crowdsourcing label quality: A theoretical study. Science China: Information Sciences, 2015, 58(11): 112103.

  • C. Qian, Y. Yu, and Z.-H. Zhou. Variable solution structure can be helpful in evolutionary optimization. Science China: Information Sciences, i2015, 58(11): 112105.

  • 李武军,周志华. 大数据哈希学习:现状与趋势. 中国科学通报, 60: 485-490, 2015. (受邀文章).

Top

2014

[Conference Paper][Journal Article]

Conference Paper

  • Z.-H. Zhou. Large margin distribution learning. In: Proceedings of the 6th IAPR International Workshop on Artifical Neural Networks in Pattern Recognition (ANNPR'14), Montreal, Canada, LNAI 8774, 2014, pp.1-11. (keynote article)

  • Y. Zhu, J. Wu, Y. Jiang, and Z.-H. Zhou. Learning with augmented multi-instance view. In: Proc. of the 6th Asian Conference on Machine Learning (ACML'14), Nha Trang, Vietnam, 2014, JMLR: W&CP, pp.234-249.

  • X.-S. Wei, J. Wu, and Z.-H. Zhou. Scalable multi-instance learning. In: Proc. IEEE International Conference on Data Mining (ICDM 2014), Shenzhen, China, 2014, pp.1037-1042.

  • Y. Zhou, W. Lin, H. Su, J. Wu, J. Wang, and Y. Zhou. Representing and recognizing motion trajectories: A tube and droplet approach. In: Proc. 22nd ACM International Conference on Multimedia (MM 2014), Orlando, Florida, 2014.

  • G. Lin, C. Shen, and J. Wu. Optimizing ranking measures for compact binary code learning. In: Proc. The 13th European Conference on Computer Vision (ECCV 2014), Zürich, Switzerland, 2014, pp.613-627.

  • W. Wang, W. Lin, Y. Chen, J. Wu, J. Wang, and B. Sheng. Finding coherent motions and semantic regions in crowd scenes: A diffusion and clustering approach. In: Proc. The 13th European Conference on Computer Vision (ECCV 2014), Zürich, Switzerland, 2014, pp.756-771.

  • C. Zhang, J. Luo, and J. Wu. A Dual-sensor enabled indoor localization system with crowdsensing spot survey. In: Proc. The 10th IEEE/ACM International Conference on Distributed Computing in Sensor Systems (DCOSS 2014), Marina Del Rey, California, 2014, pp.75-82.

  • N. Li, R. Jin, and Z.-H. Zhou. Top rank optimization in linear time. In: Advances in Neural Information Processing Systems 27 (NIPS’2014), Montreal, Canada, 2014, pp.1502-1510.

  • C. Xie, L. Yan, W.-J. Li, and Z. Zhang. Distributed power-law graph computing: Theoretical and empirical analysis. In: Advances in Neural Information Processing Systems 27 (NIPS’2014), Montreal, Canada, 2014, pp.1673-1681.

  • Z.-Q. Yu, X.-J. Shi, L. Yan, and W.-J. Li. Distributed stochastic ADMM for matrix factorization. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM), Shanghai, China 2014, pp.1259-1268.

  • Y. Fu, H. Xiong, Y. Ge, Z. Yao, Y. Zheng, and Z.-H. Zhou. Exploiting geographic dependencies for real estate appraisal: A mutual perspective of ranking and clustering. In: Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2014), New York, NY, 2014, pp.1047-1056.

  • J. Zhang, Z.-H. Zhou, and P. S. Yu. Meta-path based multi-network collective link prediction. In: Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2014), New York, NY, 2014, pp.1286-1295.

  • T. Zhang and Z.-H. Zhou. Large margin distribution machine. In: Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2014), New York, NY, 2014, pp.313-322.

  • L. Yan, W.-J. Li, G.-R. Xue, and D.-Y. Han. Coupled group lasso for web-scale CTR prediction in display advertising. In: Proc. The 31st International Conference on Machine Learning (ICML 2014), Beijing, China, 2014, pp.802-810.

  • Y. Yi, L. Zhang, J. Wang, R. Jin, and A. Jain. A single-pass algorithm for efficiently recovering sparse cluster centers for high-dimensional Data. In: Proc. The 31st International Conference on Machine Learning (ICML 2014), Beijing, China, 2014, pp.658-666.

  • L. Zhang, J. Yi, and R. Jin. Efficient algorithms for robust one-bit compressive sensing. In: Proc. The 31st International Conference on Machine Learning (ICML 2014), Beijing, China, 2014, pp.820-828.

  • Q. Da, Y. Yu, and Z.-H. Zhou. Learning with augmented class by exploiting unlabeled data. In: Proc. The 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Quebec city, Canada, 2014, pp.1760-1766.

  • S.-J. Huang, W. Gao, and Z.-H. Zhou. Fast multi-instance multi-label learning. In: Proc. The 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Quebec City, Canada, 2014, pp.1868-1874.

  • S.-Y. Li, Y. Jiang, and Z.-H. Zhou. Partial multi-view clustering. In: Proc. The 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Quebec City, Canada, 2014, pp.1968-1974.

  • C.-T. Nguyen, X. Wang, J. Liu, and Z.-H. Zhou. Labeling complicated objects: Multi-view multi-instance multi-label learning. In: Proc. The 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Quebec City, Canada, 2014, pp.2013-2019.

  • D.-Q. Zhang and W.-J. Li. Large-scale supervised multimodal hashing with semantic correlation maximization. In Proc. The 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Quebec City, Canada, 2014, pp.2177-2183.

  • W.-J. Zhang and Z.-H. Zhou. Multi-instance learning with distribution change. In: Proc. The 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Quebec City, Canada, 2014, pp.2184-2190.

  • P.-C. Zhang, W. Zhang, W.-J. Li, and M.-Y. Guo. Supervised hashing with latent factor models. In Proc. The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2014), Gold Coast, Australia, 2014, pp.173-182.

  • Y. Yu. and H. Qian. The sampling-and-learning framework: A statistical view of evolutionary algorithms. In: Proc. The 2014 IEEE Congress on Evolutionary Computation (CEC 2014), Beijing, China, 2014, pp.149-158.

  • T. Chen, Q. Guo, K. Tang, O. Temam, Z. Xu, Z.-H. Zhou, and Y. Chen. ArchRanker: A ranking approach to design space exploration. In: Proc. The 41st International Symposium on Computer Architecture (ISCA 2014), Minneapolis, MN, 2014, pp.85-96.

  • J. Wu, Y. Zhang, and W.-Y. Lin. Towards good practices for action video encoding. In: Proc. The IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2014), Columbus, OH, USA, 2014, pp.2577-2584.

  • Y. Zhang, J. Wu, and J.-F. Cai. Compact representation for image classification: To choose or to compress?. In: Proc. The IEEE Int'l Conference on Computer Vision and Pattern Recognition (CVPR 2014), Columbus, Ohio, USA, 2014, pp.907-914.

  • Q. Da, Y. Yu, and Z.-H. Zhou. Napping for functional representation of policy. In: Proc. The 2014 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2014), Paris, France, 2014, pp.189-196.

Top

Journal Article

  • J. Liu, Y. Jiang, Z.-C. Li, Z.-H. Zhou, and H.-Q. Lu. Partially shablack latent factor learning with multi-view data. IEEE Transactions on Neural Networks and Learning Systems, accepted.

  • H. Wang and W.-J. Li. Relational collaborative topic regression for recommender systems. IEEE Transactions on Knowledge and Data Engineering (TKDE). Accepted.

  • J.-H. Hu, D.-C. Zhan, X.-T. Wu, Y. Jiang, and Z.-H. Zhou. Pairwised specific distance learning from physical linkages. ACM Transactions on Knowledge Discovery from Data, accepted.

  • Q. Guo, T.-S. Chen, Z.-H. Zhou, O. Temam, L. Li, D. Qian, and Y.-J. Chen. Robust design space modeling. ACM Transactions on Design Automation of Electronic Systems, accepted.

  • J.-S. Wu, S.-J. Huang, and Z.-H. Zhou. Genome-wide protein function pblackiction through multi-instance multi-label learning. ACM/IEEE Transactions on Computational Biology and Bioinformatics, accepted.

  • Z.-H. Zhou, N. V. Chawla, Y.-C. Jin, and G. J. Williams. Big data opportunities and challenges: Discussions from data analytics perspectives. IEEE Computational Intelligence Magazine, accepted.

  • C. Zhang, K.-P. Subbu, J. Luo, and J. Wu. GROPING: Geomagnetism and cROwdsensing Poweblack Indoor NaviGation. IEEE Transactions on Mobile Computing.

  • L. Zhang, M. Mahdavi, R. Jin, T. Yang, and S. Zhu. Random Projections for Classification: A Recovery Approach. IEEE Transactions on Information Theory (TIT), 2014, 60(11): 7300 - 7316.

  • Y. Zhang, J. Wu, J. Cai, and W. Lin. Flexible Image Similarity Computation Using Hyper-Spatial Matching. IEEE Transactions on Image Processing, 2014, 23(9): 4112-4125.

  • Z. Zhang, C. Chen, G. Dai, W.-J. Li, and D.-Y. Yeung. Multicategory large margin classification methods: hinge losses vs. coherence functions. Artificial Intelligence, 2014, 215: 55-78.

  • Y.-H. Zhang, W.-Y. Lin, B. Zhou, Z.-Z. Chen, B. Sheng, and J. Wu. Facial expression cloning with elastic and muscle models. Journal of Visual Communication and Image Representation, 2014, 25(5): 916-927.

  • Y. Xiao, J. Wu, and J.-S. Yuan. mCENTRIST: A multi-channel feature generation mechanism for scene categorization. IEEE Transactions on Image Processing, 2014, 23(2): 823-836.

  • K. Zhang, B. Schölkopf, K. Muandet, Z. Wang, Z.-H. Zhou, and C. Persello. Single-source domain adaptation with target and conditional shift. In: J. A. K. Suykens, M. Signoretto, A. Argyriou, eds. Regularization, Optimization, Kernels, and Support Vector Machines, Boca Raton, FL: CRC Press, 2014, 428-456.

Top

2013

  • Z.-H. Zhou, F. Roli, and J. Kittler, eds. Multiple Classifier Systems (Lecture Notes in Artificial Intelligence 7872) - Proceedings of the 11th International Workshop on Multiple Classifier Systems (MCS'13), Berlin: Springer, 2013. ISBN: 978-3-642-38066-2

  • Z.-H. Zhou and F. Schwenker, eds. Partially Supervised Learning (Lecture Notes in Artificial Intelligence 8183) - Proceedings of the 2nd International Workshop on Partially Supervised Learning (PSL'13), Berlin: Springer, 2013. ISBN: 978-3-642-40705-5

  • C. Sun, F. Fang, Z.-H. Zhou, W. Yang, Z. Liu, eds. Intelligent Science and Big Data Engineering (Lecture Notes in Computer Science 8261) – Revised Selected Papers of the 4th International Conference on Intelligent Science and Big Data Engineering (IScIDE'13), Berlin: Springer, 2013. ISBN: 978-3- 642-42056-6

  • B. Huet, C.-W. Ngo, J. Tang, Z.-H. Zhou, A. G. Hauptmann, and S. Yan, eds. Advances in Multimedia Information Processing (Lecture Notes in Computer Science 8294) – Proceedings of the 14th Pacific-Rim Conference on Multimedia (PCM'13), Berlin: Springer, 2013. ISBN: 978-3-319-03730-1

  • X.-Y. Liu and Z.-H. Zhou. Ensemble methods for class imbalance learning. In: H. He, Y. Ma, eds. Imbalanced Learning: Foundations, Algorithms, and Applications, Hoboken, NJ: Wiley-IEEE, 2013, 61-82.

  • Z.-H. Zhou. Ensemble Methods: Foundations and Algorithms, Boca Raton, FL: Chapman & Hall/CRC, 2012. (ISBN 978-1-439-830031)

  • W. Gao and Z.-H. Zhou. On the consistency of multi-label learning. Artificial Intelligence, 2013, 199-200: 22-44.

  • W. Gao and Z.-H. Zhou. On the doubt about margin explanation of boosting. Artificial Intelligence, 2013, 203: 1-18.

  • C. Qian, Y. Yu, and Z.-H. Zhou. An analysis on recombination in multi-objective evolutionary optimization. Artificial Intelligence, 2013, 204: 99-119.

  • Y.-F. Li, I. W. Tsang, J. T. Kwok, and Z.-H. Zhou. Convex and scalable weakly labeled SVMs. Journal of Machine Learning Research, 2013, 14: 2151-2188.

  • N. Li, I. W. Tsang, and Z.-H. Zhou. Efficient optimization of performance measures by classifier adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1370-1382.
  • X. Geng, C. Yin, and Z.-H. Zhou. Facial age estimation by label distribution learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2401-2412.

  • X. Kong, M. K. Ng, and Z.-H. Zhou. Transductive multilabel learning via label set propagation. IEEE Transactions on Knowledge and Data Engineering, 25(3): 704-719.

  • J. Wu, N. Liu, C. Geyer, and J. M. Rehg. C-4: A Real-time Object Detection Framework. IEEE Transactions on Image Processing, 2013, 22(10), 4096-4107.

  • S. Wang, Z.-H. Zhou, M. Ge, and C. Wang. Resource allocation for heterogeneous multiuser cognitive radio networks with imperfect spectrum sensing. IEEE Journal on Selected Areas in Communications, 2013, 31(3): 464-475.

  • W. Lin, H. Chu, J. Wu, B. Sheng, and Z. Chen. A Heat-Map-based Algorithm for Recognizing Group Activities in Videos. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(11), 1980-1992.

  • M.-L. Zhang and Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. Data Mining and Knowledge Discovery, 2013, 26(1): 98-129.

  • F. Song, X. Tan, S. Chen, and Z.-H. Zhou. A literature survey on robust and efficient eye localization in real-life scenarios. Pattern Recognition, 2013, 46(12): 3157-3173.

  • T.-S Chen, Y.-J Chen, Q. Guo, Z.-H. Zhou, L. Li, Z.-W Xu. Effective and efficient microprocessor design space exploration using unlabeled design configurations. ACM Transactions on Intelligent Systems and Technology, 2013, 5(1): Article 20.

  • R. Jin, T. Yang, M. Mahdavi, Y.-F. Li, and Z.-H. Zhou. Improved bounds for the Nyström method with application to kernel classification. IEEE Transactions on Information Theory, 2013, 59(10): 6939-6949.

  • J.-S. Wu and Z.-H. Zhou. Sequence-based prediction of microRNA-binding residues in proteins using cost-sensitive laplacian support vector machines. ACM/IEEE Transactions on Computational Biology and Bioinformatics, 2013, 10(3): 752-759.

  • L. Wu, X. Wu, A. Lu, and Z.-H. Zhou. A spectral approach to detecting subtle anomalies in graphs. Journal of Intelligent Information Systems, 2013, 41(2): 313-337.

  • W. Gao, R. Jin, S. Zhu, and Z.-H. Zhou. One-pass AUC optimization. In: Proceedings of the 30th International Conference on Machine Learning (ICML'13), Atlanta, GA, 2013, JMLR: W&CP 28(3), pp.906-914.

  • W. Gao and Z.-H. Zhou. Uniform convergence, stabiliby and learnability for ranking problems. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI'13), Beijing, China, 2013, pp.1337-1343.

  • C.-T. Nguyen, D.-C. Zhan, and Z.-H. Zhou. Multi-modal image annotation with multi-instance multi-label LDA. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI'13), Beijing, China, 2013, pp.1558-1564.

  • S.-J. Yang, Y. Jiang, and Z.-H. Zhou. Multi-instance multi-label learning with weak label. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI'13), Beijing, China, 2013, pp.1862-1868.

  • H. Yang, and J. Wu. Reduced Heteroscedasticity Linear Regression for Nystrom Approximation. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI'13), Beijing, China, 2013, pp.1841-1847.

  • M. Xu, Y.-F. Li, and Z.-H. Zhou. Multi-label learning with PRO loss. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI'13), Bellevue, WA, 2013, pp.998-1004.

  • M. Xu, R. Jin, and Z.-H. Zhou. Speedup matrix completion with side information: Application to multi-label learning. In: Advances in Neural Information Processing Systems 26 (NIPS'13) (Lake Tahoe, NV), Cambridge, MA: MIT Press, 2013.

  • X.-Y. Liu, Q.-Q. Li, and Z.-H. Zhou. Learning imbalanced multi-class data with optimal dichotomy weights. In: Proceedings of the 13th IEEE International Conference on Data Mining (ICDM'13), Dallas, TX, 2013.

  • S.-J. Huang and Z.-H. Zhou. Active query driven by uncertainty and diversity for incremental multi-label learning. In: Proceedings of the 13th IEEE International Conference on Data Mining (ICDM'13), Dallas, TX, 2013.

  • W. Wang and Z.-H. Zhou. Co-training with insufficient views. In: Proceedings of the 5th Asian Conference on Machine Learning (ACML'13), Canberra, Australia, 2013, JMLR: W&CP 29, pp.467-482.

  • C. Chen, D. Zhang, Z.-H. Zhou, N. Li, T. Atmaca, S. Li. B-Planner: Night bus route planning using large-scale taxi GPS traces. In: Proceedings of the 11th IEEE International Conference on Pervasive Computing and Communications (PerCom'13), San Diego, CA, 2013, pp.225-233.

  • N. Li and Z.-H. Zhou. Selective ensemble of classifier chains. In: Proceedings of the 11th International Workshop on Multiple Classifier Systems (MCS'13), LNCS 7872, Nanjing, China, 2013, pp.146-156.

  • Q. Da, Y. Yu, and Z.-H. Zhou. Self-practice imitation learning from weak policy. In: Proceedings of the 2nd International Workshop on Partial Supervised Learning (PSL'13), LNAI 8183, Nanjing, China, 2013, pp.9-20.

  • T. Li and M. Li. PerGrab: Adapting grabbing gesture recognition for personalized non-contact HCI. In: Proceedings of the 4th International Conference on Intelligence Science and Big Data Engineering(IScIDE'13), LNCS 8261, Beijing, China, 2013, pp.740-747.

  • M.-Y. Shi and D.-C. Zhan. Multi-gesture recognition: a tracking learning detection approach. In : Proceedings of the 4th International Conference on Intelligence Science and Big Data Engineering(IScIDE'13), LNCS 8261, Beijing, China, 2013, pp.714-721.

  • 王魏, 周志华. 多视图在利用未标记数据学习中的效用. 见: 张长水, 杨强 主编. 机器学习及其应用2013, 北京: 清华大学出版社, 2013, 27-45.

  • 周志华. 基于分歧的半监督学习. 自动化学报, 2013, 39(11): 1871-1878. (自动化学报创刊五十周年特刊特邀论文)

  • 钱煜, 俞扬, 周志华. 一种基于自生成样本学习的奖赏塑形方法. 软件学报, 2013, 24(11): 2667-2675.

  • 胡菊花, 姜远, 周志华. 一种基于教学模型的协同训练方法. 计算机研究与发展, 2013, 50(11): 2262-2268.

Top

2012

  • Z.-H. Zhou. Ensemble Methods: Foundations and Algorithms, Boca Raton, FL: Chapman & Hall/CRC, 2012. (ISBN 978-1-439-830031)

  • Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, and Y.-F. Li. Multi-instance multi-label learning. Artificial Intelligence, 2012, 176(1): 2291-2320.

  • Y. Yu, X. Yao, and Z.-H. Zhou. On the approximation ability of evolutionary optimization with application to minimum set cover. Artificial Intelligence, 2012, 180-181: 20-33.

  • F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data, 2012, 6(1): Article 3.

  • Y.-X. Li, S. Ji, S. Kumar, J. Ye, and Z.-H. Zhou. Drosophila gene expression pattern annotation through multi-instance multi-label learning. ACM/IEEE Transactions on Computational Biology and Bioinformatics, 2012, 9(1): 98-112.

  • Y. Wang, S. Chen, and Z.-H. Zhou. A new semi-supervised classification method based on modified cluster assumption. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(5): 689-702.

  • C. He, Y.-X. Li, G. Zhang, Z. Gu, R. Yang, J. Li, Z. J. Lu, Z.-H. Zhou, C. Zhang, and J. Wang. MiRmat: Mature microRNA sequence prediction. PLOS One, 2012, 7(12): e51673.

  • L. Yuan, A. Woodard, S. Ji, Y. Jiang, Z.-H. Zhou, S. Kumar, and J. Ye. Learning sparse representation for fruit-fly gene expression pattern image annotation and retrieval. BMC Bioinformatics, 2012, 13: 107.

  • M. Li, H. Zhang, R. Wu, and Z.-H. Zhou. Sample-based software defect prediction with active and semi-supervised learning. Automated Software Engineering, 2012, 19(2): 201-230.

  • W. Wang and Z.-H. Zhou. Learnability of multi-instance multi-label learning. Chinese Science Bulletin, 2012, 57(19): 2488-2491.

  • C. Chen, J. Zhang, X. He, and Z.-H. Zhou. Non-parametric kernel learning with robust pairwise constraints. International Journal of Machine Learning and Cybernetics, 2012, 3(2): 83-96.

  • L. Wu, X. Ying, X. Wu, A. Lu, and Z.-H. Zhou. Examining spectral space of complex networks with positive and negative links. International Journal of Social Network Mining, 2012, 1(1): 91-111.

  • T. Yang, Y.-F. Li, M. Mahdavi, R. Jin and Z.-H. Zhou. Nystr?m method vs random Fourier features: A theoretical and empirical comparison. In: Advances in Neural Information Processing Systems 25 (NIPS'12) (Lake Tahoe, NV), P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger, eds. Cambridge, MA: MIT Press, 2012, pp.485-493.

  • G. Liu, J. Wu and Z.-H. Zhou. Key instance detection in multi-instance learning. In: Proceedings of the 4th Asian Conference on Machine Learning (ACML'12), Singapore, 2012, JMLR: W&CP 25, pp.253-268.

  • X.-S. Xu, Y. Jiang, X. Xue and Z.-H. Zhou. Semi-supervised multi-instance multi-label learning for video annotation task. In: Proceedings of the 20th ACM International Conference on Multimedia (MM'12), Nara, Japan, 2012, pp.737-740. (short paper)

  • N. Li, Y. Yu, and Z.-H. Zhou. Diversity regularized ensemble pruning. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'12), Bristol, UK, LNCS 7523, 2012, pp330-345.

  • C. Qian, Y. Yu, and Z.-H. Zhou. On algorithm-dependent boundary case identification for problem classes. In: Proceedings of the 12th International Conference on Parallel Problem Solving from Nature (PPSN'12), Taormina, Italy, LNCS 7491, 2012, pp.62-71.

  • S.-J. Huang, Y. Yu, and Z.-H. Zhou. Multi-label hypothesis reuse. In: Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'12), Beijing, China, 2012, pp.525-533. Best Poster Award

  • S.-J. Huang and Z.-H. Zhou. Multi-label learning by exploiting label correlations locally. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI'12), Toronto, Canada, 2012, pp.949-955.

  • Y.-F. Li, J.-H. Hu, Y. Jiang, and Z.-H. Zhou. Towards discovering what patterns trigger what labels. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI'12), Toronto, Canada, 2012, pp.1012-1018.

  • B. Wang, J. Jiang, W. Wang, Z.-H. Zhou, and Z. Tu. Unsupervised metric fusion by cross diffusion. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'12), Providence, RI, 2012, pp.2997-3004.

  • S. Wang, Z.-H. Zhou, M. Ge, and C. Wang. Resource allocation for heterogeneous multiuser OFDM-based cognitive radio networks with imperfect spectrum sensing. In: Proceedings of the 31st IEEE International Conference on Computer Communications (INFOCOM'12), Orlando, FL, 2012, pp.2264-2272.

  • Q. Li, X. Wang, W. Wang, Y. Jiang, Z.-H. Zhou, and W. Tu. Disagreement-based multi-system tracking. In: Proceedings of the ACCV Workshop on Detection and Tracking in Challenging Environments (DTCE'12), in conjunction with ACCV'12, Daejeon, Korea, 2012.

  • T. R. Hoens, Q. Qian, N. V. Chawla, and Z.-H. Zhou. Building decision trees for the multiclass imbalance problem. In: Proceedings of the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'12), LNAI 7301, Kuala Lumpur, Malaysia, 2012, pp.122-134.

  • Z.-H. Zhou. Unlabeled data and multiple views. In: Proceedings of the 1st IAPR TC3 Workshop on Partially Supervised Learning (PSL'11), Ulm, Germany, LNAI 7081, 2012, pp.1-7. Keynote Speech at PSL'11

  • X.-Y. Liu and Z.-H. Zhou. Towards cost-sensitive learning for real-world applications. In: Proceedings of the PAKDD 2011 International Workshops, LNAI 7104, Shenzhen, China, 2012, pp.494-505.

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2011

  • J. Cheng, J. Wang, S. Jiang, Z.-H. Zhou, E. Hancock. Special edition on semi-supervised learning for visual content analysis and understanding. Pattern Recognition, 2011, 44(10-11): 2242-2243.

  • L. Wang, M. Sugiyama, Z. Jing, C. Yang, Z.-H. Zhou, and J. Feng. A refined margin analysis for boosting algorithms via equilibrium margin. Journal of Machine Learning Research, 2011, 12: 1835-1863.

  • J. Du, C. X. Ling, and Z.-H. Zhou. When does co-training work in real data? IEEE Transactions on Knowledge and Data Engineering, 2011, 23(5): 788-799.

  • Y. Wang, Y. Jiang, Y. Wu, and Z.-H. Zhou. Spectral clustering on multiple manifolds. IEEE Transactions on Neural Networks, 2011, 22(7): 1149-1161.

  • J. Zhang, Q. Wang, L. He, and Z.-H. Zhou. Quantitative analysis of nonlinear embedding. IEEE Transactions on Neural Networks, 2011, 22(12): 1987-1998.

  • X. Geng, K. Smith-Miles, Z.-H. Zhou, and L. Wang. Face image modeling by multilinear subspace analysis with missing values. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2011, 41(3): 881-892.

  • M.-L. Zhang and Z.-H. Zhou. CoTrade: Confident co-training with data editing. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2011, 41(6): 1612-1626.

  • S. Wang, F. Huang, and Z.-H. Zhou. Fast power allocation algorithm for cognitive radio networks. IEEE Communications Letters, 2011, 15(8): 845-847.

  • Y. Jiang, M. Li, and Z.-H. Zhou. Software defect detection with ROCUS. Journal of Computer Science and Technology, 2011, 26(2): 328-342.

  • Z.-H. Zhou. When semi-supervised learning meets ensemble learning. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(1): 6-16.

  • Y. Ge, H. Xiong, C. Liu, and Z.-H. Zhou. A taxi driving fraud detection system. In: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM'11), Vancouver, Canada, 2011, pp.181-190.

  • W.-Z. Dai, Y. Yu, and Z.-H. Zhou. Lifted-rollout for approximate policy iteration of Markov decision process. In: Proceedings of the 11th IEEE International Conference on Data Mining Workshops (International Workshop on Learning and Data Mining for Robotics (LEMIR'11), in conjunction with ICDM'11), Vancouver, Canada, 2011, pp.689-696.

  • X.-S. Xu, X. Xue, and Z.-H. Zhou. Ensemble multi-instance multi-label learning approach for video annotation task. In: Proceedings of the 19th ACM International Conference on Multimedia (MM'11), Scottsdale, AZ, 2011, pp.1153-1156. (short paper)

  • X.-S. Xu, Y. Jiang, P. Liang, X. Xue, and Z.-H. Zhou. Ensemble approach based on conditional random field for multi-label image and video annotation. In: Proceedings of the 19th ACM International Conference on Multimedia (MM'11), Scottsdale, AZ, 2011, pp.1377-1380. (short paper)

  • D. Zhang, N. Li, Z.-H. Zhou, C. Chen, L. Sun, and S. Li. iBAT: Detecting anomalous taxi trajectories from GPS traces. In: Proceedings of the 13th ACM International Conference on Ubiquitous Computing (UbiComp'11), Beijing, China, 2011, pp.99-108.

  • W. Gao and Z.-H. Zhou. On the consistency of multi-label learning. In: Proceedings of the 24th Annual Conference on Learning Theory (COLT'11), Budapest, Hungary, 2011, JMLR: W&CP 19, pp.341-358.

  • Y.-F. Li and Z.-H. Zhou. Towards making unlabeled data never hurt. In: Proceedings of the 28th International Conference on Machine Learning (ICML'11), Bellevue, WA, 2011, pp.1081-1088.

  • Y.-F. Li and Z.-H. Zhou. Improving semi-supervised support vector machines through unlabeled instances selection. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI'11), San Francisco, CA, 2011, pp.386-391. (CORR abs/1005.1545)

  • Y. Wang, Y. Jiang, Y. Wu, and Z.-H. Zhou. Localized K-flats. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI'11), San Francisco, CA, 2011, pp.523-530.

  • Y. Wang, Y. Jiang, Y. Wu, and Z.-H. Zhou. Local and structural consistency for multi-manifold clustering. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp.1559-1564.

  • Y. Yu, Y.-F. Li, and Z.-H. Zhou. Diversity regularized machine. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp.1603-1608.

  • Q. Guo, T. Chen, Y. Chen, Z.-H. Zhou, W. Hu, and Z. Xu. Effective and efficient microprocessor design space exploration using unlabeled design configurations. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp.1671-1677.

  • L. Wu, X. Ying, X. Wu, and Z.-H. Zhou. Line orthogonality in adjacency eigenspace with application to community partition. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp.2349-2354.

  • C. Qian, Y. Yu, and Z.-H. Zhou. An analysis on recombination in multi-objective evolutionary optimization. In: Proceedings of the 13th ACM Genetic and Evolutionary Computation Conference (GECCO'11), Dublin, Ireland, 2011, pp.2051-2058. This paper won the Theory Best Paper Award at GECCO'11

  • C. Qian, Y. Yu, and Z.-H. Zhou. Collisions are helpful for computing unique input-output sequences. In: Poster Proceedings of the ACM 2011 Genetic and Evolutionary Computation Conference (GECCO'11), Dublin, Ireland, 2011, pp.265-266.

  • L. Wu, X. Ying, X. Wu, A. Lu, and Z.-H. Zhou. Spectral analysis of k-balanced signed graphs. In: Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'11), LNAI 6635, Shenzhen, China, 2011, pp.1-12.

Top

2010

  • M.-L. Zhang, G. Tsoumakas, and Z.-H. Zhou, eds. Working Notes of the 2nd Workshop on Learning from Multi-Label Data (MLD'10), in conjunction with ICML'10, Haifa, Israel, 2010.

  • Q. Yang, Z.-H. Zhou, W. Mao, W. Li, and N. N. Li. Social learning. IEEE Intelligent Systems, 2010, 25(4): 9-11.

  • Z.-H. Zhou and H. Li. Preface. Journal of Computer Science and Technology, 2010, 25(4): 1-2.

  • T. B. Ho, Z.-H. Zhou, and H. Motoda. Editorial. Intelligent Data Analysis, 2010, 14(4): 437-438.

  • Y. Zhang and Z.-H. Zhou. Multi-label dimensionality reduction via dependence maximization. ACM Transactions on Knowledge Discovery from Data, 2010, 4(3): Article 14.

  • Y. Zhang and Z.-H. Zhou. Cost-sensitive face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(10): 1758-1769.

  • J. Liu, S. Chen, Z.-H. Zhou, and X. Tan. Generalized low rank approximations of matrices revisited. IEEE Transactions on Neural Networks, 2010, 21(4): 621-632.

  • Y. Fu, Y. Guo, Y. Zhu, F. Liu, C. Song, and Z.-H. Zhou. Multi-view video summarization. IEEE Transactions on Multimedia, 2010, 12(7): 717-729.

  • Z.-H. Zhou and X.-Y. Liu. On multi-class cost-sensitive learning. Computational Intelligence, 2010, 26(3): 232-257.

  • Z.-H. Zhou and M. Li. Semi-supervised learning by disagreement. Knowledge and Information Systems, 2010, 24(3): 415-439.

  • Y. Yu and Z.-H. Zhou. A framework for modeling positive class expansion with single snapshot. Knowledge and Information Systems, 2010, 25(2): 211-227. Invited paper for the PAKDD'08 Best Paper Award

  • Y. Zhang, R. Jin, and Z.-H. Zhou. Understanding bag-of-words model: A statistical framework. International Journal of Machine Learning and Cybernetics, 2010, 1(1): 43-52.

  • M. Li, W. Wang, and Z.-H. Zhou. Exploiting remote learners in internet environment with agents. Science China: Information Sciences, 2010, 53(1): 64-76.

  • M.-L. Zhang and Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM'10), Sydney, Australia, 2010.

  • W. Wang and Z.-H. Zhou. Multi-view active learning in the non-realizable case. In: Advances in Neural Information Processing Systems 23 (NIPS'10) (Vancouver, Canada), J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta, eds. Cambridge, MA: MIT Press, 2010, 2388-2396.

  • S.-J. Huang, R. Jin, and Z.-H. Zhou. Active learning by querying informative and representative examples. In: Advances in Neural Information Processing Systems 23 (NIPS'10) (Vancouver, Canada), J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta, eds. Cambridge, MA: MIT Press, 2010, pp.892-900.

  • Y. Ge, H. Xiong, Z.-H. Zhou, H. Ozdemir, J. Yu, and K. C. Lee. TOP-EYE: Top-k evolving trajectory outlier detection. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM'10), Toronto, Canada, 2010, pp.1733-1736.

  • T. F. Liu, K. M. Ting, and Z.-H. Zhou. On detecting clustered anomalies using SCiForest. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'10), Barcelona, Spain, LNAI 6322, 2010, pp.274-290.

  • W. Gao and Z.-H. Zhou. Approximation stability and boosting. In: Proceedings of the 21st International Conference on Algorithmic Learning Theory (ALT'10), LNCS 6331, Canberra, Australia, 2010, pp.59-73.

  • Y. Yu, C. Qian, and Z.-H. Zhou. Towards analyzing recombination operators in evolutionary search. In: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN'10), LNCS 6238, Krakow, Poland, 2010, pp.144-153.

  • Y. Wang, Y. Jiang, Y. Wu, and Z.-H. Zhou. Multi-manifold clustering. In: Proceedings of the 11th Pacific Rim International Conference on Artificial Intelligence (PRICAI'10), LNAI 6230, Daegu, Korea, 2010, pp.280-291. This paper won the Best Paper Award at PRICAI'10

  • X.-Y. Liu and Z.-H. Zhou. Learning with cost intervals. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'10), Washington, DC, 2010, pp.403-412.

  • W. Wang and Z.-H. Zhou. A new analysis on co-training. In: Proceedings of the 27th International Conference on Machine Learning (ICML'10), Haifa, Israel, 2010, pp.1135-1142.

  • Y.-Y. Sun, Y. Zhang, and Z.-H. Zhou. Multi-label learning with weak label. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA, 2010, pp.593-598.

  • Y.-Y. Sun, M. Ng, and Z.-H. Zhou. Multi-instance dimensionality reduction. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA, 2010, pp.587-592.

  • Y.-F. Li, J. Kwok, and Z.-H. Zhou. Cost-sensitive semi-supervised support vector machine. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA, 2010, pp.500-505.

  • X. Geng, K. Smith-Miles, and Z.-H. Zhou. Facial age estimation by learning from label distribution. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA, 2010, pp.451-456.

  • Z.-H. Zhou and N. Li. Multi-information ensemble diversity. In: Proceedings of the 9th International Workshop on Multiple Classifier Systems (MCS'10), LNCS 5997, Cairo, Egypt, 2010, pp.134-144.

Top

2009

  • Z.-H. Zhou and T. Washio, eds. Advances in Machine Learning (Lecture Notes in Artificial Intelligence 5828). Proceedings of the 1st Asian Conference on Machine Learning (ACML'09), Berlin: Springer, 2009. ISBN: 978-3-642-05223-1

  • G. Tsoumakas, M.-L. Zhang, and Z.-H. Zhou, eds. Working Notes of the 1st Workshop on Learning from Multi-Label Data (MLD'09), in conjunction with ECML PKDD'09, Bled, Slovenia, 2009.

  • N. Chawla, N. Japkowicz, and Z.-H. Zhou, eds. Working Notes of the Workshop on Data Mining When Classes are Imbalanced and Errors Have Costs (ICEC'09), in conjunction with PAKDD'09, Bangkok, Thailand, 2009.

  • Z.-H. Zhou, H. Li, and Q. Yang. Editorial: Special issue on selected papers of PAKDD 2007. Knowledge and Information Systems, 2009, 19(2): 131-132.

  • T. B. Ho, Z.-H. Zhou, and H. Motoda. Preface. International Journal of Software and Informatics, 2009, 3(1): 1-2.

  • Z.-H. Zhou. Ensemble learning. In: S. Z. Li ed. Encyclopedia of Biometrics, Berlin: Springer, 2009, 270-273.

  • Z.-H. Zhou. Ensemble. In: L. Liu and T. Özsu eds. Encyclopedia of Database Systems, Berlin: Springer, 2009, 988-991.

  • Z.-H. Zhou. Boosting. In: L. Liu and T. Özsu eds. Encyclopedia of Database Systems, Berlin: Springer, 2009, 260-263.

  • Z.-H. Zhou and Y. Yu. AdaBoost. In: X. Wu and V. Kumar eds. The Top Ten Algorithms in Data Mining, Boca Raton, FL: Chapman & Hall, 2009, 127-149.

  • X.-B. Xue and Z.-H. Zhou. Distributional features for text categorization. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(3): 428-442.

  • Y. Guo, F. Liu, J. Shi, Z.-H. Zhou, and M. Gleicher. Image retargeting using mesh parametrization. IEEE Transactions on Multimedia, 2009, 11(5): 856-867.

  • X. Tan, S. Chen, Z.-H. Zhou, and J. Liu. Face recognition under occlusions and variant expressions with partial similarity. IEEE Transactions on Information Forensics & Security, 2009, 4(2): 217-230.

  • X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for class-imbalance learning_. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2009, 39(2): 539-550.

  • M. Li, H. Li, and Z.-H. Zhou. Semi-supervised document retrieval. Information Processing & Management, 2009, 45(3): 341-355.

  • M.-L. Zhang and Z.-H. Zhou. Multi-instance clustering with applications to multi-instance prediction. Applied Intelligence, 2009, 31(1): 47-68.

  • S. Ji, Y.-X. Li, Z.-H. Zhou, S. Kumar, J. Ye. A bag-of-words approach for drosophila gene expression pattern annotation. BMC Bioinformatics, 2009, 10: 119.

  • Y. Jiang, M. Li, and Z.-H. Zhou. Mining extremely small data sets with application to software reuse. Software: Practice and Experience, 2009, 39(4): 423-440.

  • N. Li, Y. Yu, and Z.-H. Zhou. Semi-naive exploitation of one-dependence estimators. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM'09), Miami, FL, 2009, pp.278-287.

  • L.-P. Liu, Y. Jiang, and Z.-H. Zhou. Least square incremental linear discriminant analysis. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM'09), Miami, FL, 2009, pp.298-306.

  • X. Geng, K. Smith-Miles, Z.-H. Zhou, and L. Wang. Face image modeling by multilinear subspace analysis with missing values. In: Proceedings of the 17th ACM International Conference on Multimedia (ACM Multimedia'09), Beijing, China, 2009, pp.629-632. (short paper)

  • Y.-F. Li, J. T. Kwok, I. W. Tsang, and Z.-H. Zhou. A convex method for locating regions of interest with multi-instance learning. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'09), Bled, Slovenia, Part II, LNAI 5782, 2009, pp.15-30.

  • J.-M. Xu, G. Fumera, F. Roli, and Z.-H. Zhou. Training SpamAssassin with active semi-supervised learning. In: Proceedings of the 6th Conference on Email and Anti-Spam (CEAS'09), Mountain View, CA, 2009.

  • Z.-H. Zhou, Y.-Y. Sun, and Y.-F. Li. Multi-instance learning by treating instances as non-i.i.d. samples. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1249-1256. (CORR abs/0807.1997)

  • Y.-F. Li, J. T. Kwok, and Z.-H. Zhou. Semi-supervised learning using label mean. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.633-640.

  • D.-C. Zhan, M. Li, Y.-F. Li, and Z.-H. Zhou. Learning instance specific distances using metric propagation. In: Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada, 2009, pp.1225-1232.

  • S. Ji, L. Yuan, Y.-X. Li, Z.-H. Zhou, S. Kumar, and J. Ye. Drosophila gene expression pattern annotation using sparse features and term-term interactions. In: Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'09), Paris, France, 2009, pp.407-416.

  • Y. Zhang and Z.-H. Zhou. Non-metric label propagation. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1357-1362.

  • M. Li, X.-B. Xue, and Z.-H. Zhou. Exploiting multi-modal interactions: A unified framework. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1120-1125.

  • Y.-X. Li, S. Ji, J. Ye, S. Kumar, and Z.-H. Zhou. Drosophila gene expression pattern annotation through multi-instance multi-label learning. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1445-1450.

  • Z.-H. Zhou. When semi-supervised learning meets ensemble learning. In: Proceedings of the 8th International Workshop on Multiple Classifier Systems (MCS'09), Reykjavik, Iceland, LNCS 5519, 2009, pp.529-538. Invited plenary talk at MCS'09

  • N. Li and Z.-H. Zhou. Selective ensemble under regularization framework. In: Proceedings of the 8th International Workshop on Multiple Classifier Systems (MCS'09), Reykjavik, Iceland, LNCS 5519, 2009, pp.293-303.

  • R. Jin, S. Wang, and Z.-H. Zhou. Learning a distance metric from multi-instance multi-label data. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, 2009, pp.896-902.

  • X. Tan, F. Song, Z.-H. Zhou, and S. Chen. Enhanced pictorial structures for precise eye localization under uncontrolled conditions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, 2009, pp.1621-628.

  • Y.-F. Li, I. W. Tsang, J. Kwok, and Z.-H. Zhou. Tighter and convex maximum margin clustering. In: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS'09), Clearwater Beach, FL, 2009, pp.328-335.

  • Z.-H. Zhou, M. Ng, Q.-Q. She, and Y. Jiang. Budget semi-supervised learning. In: Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'09), Bangkok, Thailand, LNAI 5476, 2009, pp.588-595.

  • C. X. Ling, J. Du, and Z.-H. Zhou. When does co-training work in real data? In: Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'09), Bangkok, Thailand, LNAI 5476, 2009, pp.596-603.

  • X.-Y. Liu and Z.-H. Zhou. Learning with cost intervals. In: Working Notes of the Workshop on Data Mining When Imbalanced Classes and Errors in Costs (ICEC'09), in conjunction with PAKDD'09, Bangkok, Thailand, 2009, pp.26-37.

  • 周志华, 王珏 主编. 机器学习及其应用:2009, 北京: 清华大学出版社, 2009. (ISBN 978-7-302-20419-0)

  • 周志华, 张敏灵. MIML: 多示例多标记学习. 见: 周志华, 王珏 主编, 机器学习及其应用:2009, 北京: 清华大学出版社, 2009, 218-234 (第10章).

Top

2008

  • Carlos Soares, Yonghong Peng, Jun Meng, Takashi Washio, Zhi-Hua Zhou, eds. Applications of Data Mining in E-Business and Finance. Revised Selected Papers of PAKDD'07 Workshop on Data Mining for Business, Amsterdam, The Netherlands: IOS Press, 2008.

  • Tu Bao Ho, Zhi-Hua Zhou, eds. PRICAI 2008: Trends in Artificial Intelligence (Lecture Notes in Artificial Intelligence 5351). Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence (PRICAI'08), Berlin: Springer, 2008.

  • Carlos Soares, Yonghong Peng, Jun Meng, Takashi Washio, Zhi-Hua Zhou. Applications of data mining in e-business and finance: Introduction. In: Soares C, Peng Y, Meng J, Washio T, Zhou Z-H, eds. Applications of Data Mining in E-Business and Finance, Amsterdam, The Netherlands: IOS Press, 2008, 1-9.

  • Yang Yu, Zhi-Hua Zhou. A new approach to estimating the expected first hitting time of evolutionary algorithms. Artificial Intelligence, 2008, 172(15): 1809-1832.

  • Fei Tony Liu, Kai Ming Ting, Yang Yu, Zhi-Hua Zhou. Spectrum of variable-random trees. Journal of Artificial Intelligence Research, 2008, 32: 355-384.

  • Xiao-Bing Xue, Zhi-Hua Zhou, Zhongfei (Mark) Zhang. Improving web search using image snippets. ACM Transactions on Internet Technology, 2008, 8(4): Article 21.

  • Xin Geng, Zhi-Hua Zhou, Kate Smith-Miles. Individual stable space: An approach to face recognition under uncontrolled conditions. IEEE Transactions on Neural Networks, 2008, 19(8): 1354-1368.

  • Daoqiang Zhang, Songcan Chen, Zhi-Hua Zhou. Constraint score: A new filter method for feature selection with pairwise constraints. Pattern Recognition, 2008, 41(5): 1440-1451.

  • Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg. Top 10 algorithms in data mining. Knowledge and Information Systems, 2008, 14(1): 1-37.

  • Daoqiang Zhang, Songcan Chen, Zhi-Hua Zhou, Qiang Yang. Constraint projections for ensemble learning. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08), Chicago, IL, 2008, 758-763.

  • Yin Zhang, Zhi-Hua Zhou. Multi-label dimensionality reduction via dependence maximization. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08), Chicago, IL, 2008, 1503-1505.

  • Wei Wang, Zhi-Hua Zhou. On multi-view active learning and the combination with semi-supervised learning. In: Proceedings of the 25th International Conference on Machine Learning (ICML'08), Helsinki, Finland, 2008, 1152-1159.

  • Liwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua Zhou, Jufu Feng. On the margin explanation of boosting algorithm. In: Proceedings of the 21st Annual Conference on Learning Theory (COLT'08), Helsinki, Finland, 2008, 479-490.

  • Yin Zhang, Zhi-Hua Zhou. Cost-sensitive face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'08), Anchorage, AK, 2008.

  • Xin Geng, Kate Smith-Miles, Zhi-Hua Zhou. Facial age estimation by nonlinear aging pattern subspace. In: Proceedings of the 16th ACM International Conference on Multimedia (ACM Multimedia'08), Vancouver, Canada, 2008, 721-724.

  • A. B. Goldburg, M. Li, and X. Zhu. On manifold regularization: A new learning setting and empirical study. In: Procceedings of the 19th European Conference on Machine Learning (ECML'08), LNAI 5211, Antwerp, Belgium, 2008, pp. 393-407.
  • Min-Ling Zhang, Zhi-Hua Zhou. M3MIML: A maximum margin method for multi-instance multi-label learning. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, 688-697.

  • Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou. Isolation forest. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, 413-422. (Image This paper won the IEEE ICDM'08 Theoretical/Algorithms Runner-Up Best Paper Award)

  • Li-Ping Liu, Yang Yu, Yuan Jiang, Zhi-Hua Zhou. TEFE: A time-efficient approach to feature extraction. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, 423-432.

  • Yang Yu, Zhi-Hua Zhou. A framework for modeling positive class expansion with single snapshot. In: Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, Japan, LNAI 5012, Washio T, Suzuki E, Ting K M, Inokuchi A, eds. Berlin: Springer, 2008, 429-440. (Image This paper won the PAKDD'08 Best Paper Award)

  • Ming Li, Zhongfei (Mark) Zhang, Zhi-Hua Zhou. Mining bulletin board systems using community generation. In: Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, Japan, LNAI 5012, Washio T, Suzuki E, Ting K M, Inokuchi A, eds. Berlin: Springer, 2008, 209-221.

  • Zhi-Hua Zhou. Semi-supervised learning by disagreement. In: Proceedings of the 4th IEEE International Conference on Granular Computing (GrC'08), Hangzhou, China, 2008, 93. Invited Talk at GrC'08

  • 周志华. 半监督学习专刊前言. 软件学报, 2008, 19(11): 2789-2790.

  • 李小琳,周志华. 一种从不完备关系数据中学习PRM的方法. 软件学报, 2008, 19(1): 73-81.

  • 张宇,周志华. 基于集成的年龄估计方法. 自动化学报, 2008, 34(8): 997-1000.

  • 姜远,佘俏俏,黎铭,周志华. 一种直推式多标记文档分类方法. 计算机研究与发展, 2008, 45(11): 1817-1823

  • 黎铭,周志华. 基于多核集成的在线半监督学习方法. 计算机研究与发展, 2008, 45(12): 2060-2068. (计算机研究与发展创刊五十周年特刊特邀论文)

  • 周志华. 新时期研究生课程教学的实践与探索. 计算机教育, 2008, 11月, 第20期, 87-89. (Image 获2008英特尔杯全国计算机教育优秀论文评比优秀奖)

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2007

  • Z.-H. Zhou, H. Li, and Q. Yang, eds. Advances in Knowledge Discovery and Data Mining (Lecture Notes in Artificial Intelligence 4426). Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'07), Berlin: Springer, 2007. ISBN: 978-3-540-71700-3.

  • T. Washio, Z.-H. Zhou, J. Z. Huang, X. Hu, J. Li, C. Xie, J. He, D. Zou, K.-C. Li, M. M. Freire, eds. Emerging Technologies in Knowledge Discovery and Data Mining (Lecture Notes in Artificial Intelligence 4819). Revised Selected Papers of PAKDD'07 International Workshops, Berlin: Springer, 2007. ISBN: 978-3-540-77016-9.

  • R. Lu and Z.-H. Zhou. Advances of Artificial intelligence and Knowledge Engineering in China. The Computer Journal, 2007, 50(4): 377. This article was the editorial to the special issue Advances of Artificial Intelligence and Knowledge Engineering in China edited by R. Lu and Z.-H. Zhou

  • X. Geng, Z.-H. Zhou , and K. Smith-Miles. Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2007, 29(12): 2234-2240. This paper was listed as the " Featured article " of the vol.29 no.12 issue of TPAMI

  • Z.-H. Zhou and M. Li. Semi-supervised regression with co-training style algorithms . IEEE Transactions on Knowledge and Data Engineering , 2007, 19(11): 1479-1493.

  • M. Li and Z.-H. Zhou . _Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples . IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans , 2007, 37(6): 1088-1098.

  • M.-L. Zhang and Z.-H. Zhou . ML-kNN: A lazy learning approach to multi-label learning . Pattern Recognition , 2007, 40(7): 2038-2048.

  • Z.-H. Zhou and M.-L. Zhang. Solving multi-instance problems with classifier ensemble based on constructive clustering . Knowledge and Information Systems , 2007, 11(2): 155-170.

  • Y. Yu, D.-C. Zhan, X.-Y. Liu, M. Li, and Z.-H. Zhou . Predicting future customers via ensembling gradually expanded trees . International Journal of Data Warehousing and Mining , 2007, 3(2): 12-21. Invited paper for the PAKDD'06 Data Mining Competition (Open Category) Grand Champion Team

  • D. Zhang, S. Chen, and Z.-H. Zhou . Entropy-inspired competitive clustering algorithms . International Journal of Software Informatics , 2007, 1(1): 67-84.

  • Z.-H. Zhou and H.-B. Dai. Exploiting image contents in web search . In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI'07) , Hyderabad, India, 2007, pp.2928-2933.

  • Z.-H. Zhou , D.-C. Zhan, and Q. Yang. Semi-supervised learning with very few labeled training examples . In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI'07) , Vancouver, Canada, 2007, pp.675-680.

  • M.-L. Zhang and Z.-H. Zhou . Multi-label learning by instance differentiation . In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI'07) , Vancouver, Canada, 2007, pp.669-674.

  • Z.-H. Zhou and J.-M. Xu. On the relation between multi-instance learning and semi-supervised learning . In: Proceedings of the 24th International Conference on Machine Learning (ICML'07) , Corvallis, OR, 2007, pp.1167-1174.

  • X.-L. Li and Z.-H. Zhou . Structure learning of probabilistic relational models from incomplete relational data . In: Proceedings of the 18th European Conference on Machine Learning (ECML'07) , Warsaw, Poland, LNAI 4701, 2007, pp.214-225.

  • W. Wang and Z.-H. Zhou . Analyzing co-training style algorithms . In: Proceedings of the 18th European Conference on Machine Learning (ECML'07) , Warsaw, Poland, LNAI 4701, 2007, pp.454-465.

  • Y. Yu, Z.-H. Zhou , and K. M. Ting. Cocktail ensemble for regression . In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM'07) , Omeha, NE, 2007, pp.721-726.

  • D. Zhang, Z.-H. Zhou , and S. Chen. Semi-supervised dimensionality reduction . In: Proceedings of the 7th SIAM International Conference on Data Mining (SDM'07) , Minneapolis, MN, 2007, pp.629-634.

  • J. Liu, S. Chen, Z.-H. Zhou , and X. Tan. Single image subspace for face recognition . In: Proceedings of the 3rd International Workshop on Analysis and Modeling of Faces and Gestures (AMFG'07) , in Conjunction with ICCV'07, Rio de Janeiro, Brazil, LNCS 4778, 2007, pp.205-219.

  • Z.-H. Zhou . Mining ambiguous data with multi-instance multi-label learning . In: Proceedings of the 3rd International Conference on Advanced Data Mining and Applications (ADMA'07) , Harbin, China, LNAI 4632, 2007, pp.1.Invited talk at ADMA'07

  • 周志华 , 王珏 主编 . 机器学习及其应用: 2007 , 北京 : 清华大学出版社 , 2007 (ISBN 978-7-302-16076-2).

  • 周志华 . 半监督学习中的协同训练风范 . 见 : 周志华 , 王珏 主编 . 机器学习及其应用: 2007 , 北京 : 清华大学出版社 , 2007, 259-275 .

  • 周志华 . 机器学习与数据挖掘 . 中国计算机学会通讯 , 2007, 3 (12): 35-44. (特邀综述

  • 薛晓冰 , 韩洁凌 , 姜远 , 周志华 . 基于多示例学习技术的 Web 目录页面链接推荐 . 计算机研究与发展 , 2007, 44 (3): 406-411.

  • 眭俊明 , 姜远 , 周志华 . 基于频繁项集挖掘的贝叶斯分类算法 . 计算机研究与发展 , 2007, 44 (8): 1293-1300. (Image 获 CCTA'07 优秀学生论文奖 )

  • 王魏,姜远,周志华. 对Aggregative-Learning算法的分析. 计算机研究与发展, 2007, 44(增刊II): 219-224.

  • 李楠 , 姜远 , 周志华 . 基于模型似然的超 1- 依赖贝叶斯分类器集成方法 . 模式识别与人工智能 , 2007, 20 (6): 727-737.

  • 詹德川 , 周志华 . 基于相关投影分的特征选择算法 . 计算机科学与探索 , 2007, 1 (2): 138-145.

  • 刘胥影 , 姜远 , 周志华 . 类别不平衡性对代价敏感学习的影响 . 中国人工智能学会第 12 届全国学术年会论文集 , 北京 : 北京邮电大学出版社 , 2007, 163-168. (Image 获 CAAI'07 优秀论文奖 )

Top

2006

  • A. Ghafoor, Z. Zhang, M. S. Lew, and Z.-H. Zhou. Machine learning approaches to multimedia information retrieval. ACM/Springer Multimedia Systems, 2006, 12(1): 1-2. this article was the editorial to the special issue Machine Learning Approaches to Multimedia Information Retrieval edited by A. Ghafoor, Z. Zhang, M. S. Lew, and Z.-H. Zhou

  • Z.-H. Zhou , K.-J. Chen, and H.-B. Dai. Enhancing relevance feedback in image retrieval using unlabeled data . ACM Transactions on Information Systems , 2006, 24 (2): 219-244.

  • Z.-H. Zhou and X.-Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem . IEEE Transactions on Knowledge and Data Engineering , 2006, 18 (1): 63-77.

  • M.-L. Zhang and Z.-H. Zhou . Multilabel neural networks with applications to functional genomics and text categorization . IEEE Transactions on Knowledge and Data Engineering , 2006, 18 (10): 1338-1351.

  • D. Zhang, Z.-H. Zhou , and S. Chen. Diagonal principal component analysis for face recognition . Pattern Recognition , 2006, 39 (1): 140-142.

  • D. Zhang, S. Chen, and Z.-H. Zhou . Learning the kernel parameters in kernel minimum distance classifier . Pattern Recognition , 2006, 39 (1): 133-135.

  • X. Tan, S. Chen, Z.-H. Zhou , and F. Zhang. Face recognition from a single image per person: A survey . Pattern Recognition , 2006, 39 (9): 1725-1745.

  • Z.-H. Zhou and W. Tang. Clusterer ensemble . Knowledge-Based Systems , 2006, 19 (1): 77-83.

  • M.-L. Zhang and Z.-H. Zhou . Adapting RBF neural networks to multi-instance learning . Neural Processing Letters , 2006, 23 (1): 1-26.

  • X. Geng and Z.-H. Zhou . Image region selection and ensemble for face recognition . Journal of Computer Science & Technology , 2006, 21 (1): 116-125.

  • Z.-H. Zhou . Multi-instance learning from supervised view . Journal of Computer Science & Technology , 2006, 21 (5): 800-809. Invited paper

  • Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification . In: Advances in Neural Information Processing Systems 19 (NIPS'06) (Vancouver, Canada), B. Schölkopf, J. C. Platt, and T. Hofmann, eds. Cambridge, MA: MIT Press, 2007, pp.xxx-xxx.

  • Z.-H. Zhou and X.-Y. Liu. On multi-class cost-sensitive learning . In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06) , Boston, MA, 2006, pp.567-572.

  • Y. Yu and Z.-H. Zhou . A new approach to estimating the expected first hitting time of evolutionary algorithms . In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06) , Boston, MA, 2006, pp.555-560.

  • X.-B. Xue, Z.-H. Zhou , and Z. Zhang. Improve web search using image snippets . In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06) , Boston, MA, 2006, pp.1431-1436.

  • X. Tan, S. Chen, Z.-H. Zhou , and J. Liu. Learning non-metric partial similarity based on maximal margin criterion . In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) , New York, NY, 2006, pp.138-145.

  • X.-B. Xue and Z.-H. Zhou . Distributional features for text categorization . In: Proceedings of the 17th European Conference on Machine Learning (ECML'06) , Berlin, Germany, LNAI 4212, 2006, pp.497-508.

  • Z.-H. Zhou and H.-B. Dai. Query-sensitive similarity measure for content-based image retrieval . In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06) , Hong Kong, China, 2006, pp.1211-1215.

  • X.-Y. Liu and Z.-H. Zhou . The influence of class imbalance on cost-sensitive learning: An empirical study . In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06) , Hong Kong, China, 2006, pp.970-974.

  • D. Zhang, Z.-H. Zhou , and S. Chen. Adaptive kernel principal component analysis with unsupervised learning of kernels . In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06) , Hong Kong, China, 2006, pp.1178-1182.

  • X.-Y. Liu, J. Wu, and Z.-H. Zhou . Exploratory under-sampling for class-imbalance learning . In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM'06) , Hong Kong, China, 2006, pp.965-969.

  • X. Geng, Z.-H. Zhou , Y. Zhang, G. Li, and H. Dai. Learning from facial aging patterns for automatic age estimation . In: Proceedings of the 14th ACM International Conference on Multimedia (MM'06) , Santa Barbara, CA, 2006, pp.307-316.

  • D.-C. Zhan and Z.-H. Zhou . Neighbor line-based locally linear embedding . In: Proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'06) , Singapore, LNAI 3918, 2006, pp.606-615.

  • Z.-H. Zhou . Learning with unlabeled data and its application to image retrieval . In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06) , Guilin, China, LNAI 4099, 2006, pp.5-10. Keynote speech at PRICAI'06

  • D. Zhang, Z.-H. Zhou , and S. Chen. Non-negative matrix factorization on kernels . In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06) , Guilin, China, LNAI 4099, 2006, pp.404-412. (ImageThis paper won the Best Paper Award at PRICAI'06)

  • X. Geng, Z.-H. Zhou , and H. Dai. Uncontrolled face recognition by individual stable neural network . In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06) , Guilin, China, LNAI 4099, 2006, pp.553-562.

  • D. Zhang, S. Chen, and Z.-H. Zhou . Recognizing face or object from a single image: Linear vs. kernel methods on 2D patterns . In: Proceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition (S+SSPR'06) , in conjunction with ICPR'06, Hong Kong, China, LNCS 4109, 2006, pp.889-897.

  • Y. Jiang, M. Li, and Z.-H. Zhou . Generation of comprehensible hypotheses from gene expression data . In: Proceedings of the International Workshop on Data Mining for Biomedical Application (BioDM'06) , in conjunction with PAKDD'06, Singapore, LNBI 3916, 2006, pp.116-123.

  • J. Zhang, L. He, and Z.-H. Zhou . Ensemble-based discriminant manifold learning for face recognition . In: Proceedings of the 2nd International Conference on Natural Computation (ICNC'06) , Chongqing, China, LNCS 4221, 2006, pp.29-38.

  • 王珏, 周志华, 周傲英 主编. 机器学习及其应用, 北京: 清华大学出版社, 2006 (ISBN 7-302-12038-2 / TP.7792).

  • 周志华. 选择性集成. 见: 王珏, 周志华, 周傲英 主编. 机器学习及其应用, 北京: 清华大学出版社, 2006, 170-188.

  • 周志华. 多示例学习. 见: 刘大有 主编. 知识科学中的基本问题研究, 北京: 清华大学出版社, 2006, 322-336.

  • 周志华. 规划与学习. 见: 刘大有 主编. 知识科学中的基本问题研究, 北京: 清华大学出版社, 2006, 239-240.

  • 詹德川,周志华. 基于流形学习的多示例回归算法. 计算机学报, 2006, 29(11): 1948-1955.

  • 姜远,周志华. 基于词频分类器集成的文本分类方法. 计算机研究与发展, 2006, 43(10): 1681-1687.

  • 戴宏斌,张敏灵,周志华. 一种基于多示例学习的图像检索方法. 模式识别与人工智能, 2006, 19(2): 179-185.

  • 刘胥影,吴建鑫,周志华. 一种基于级联模型的类别不平衡数据分类方法. 南京大学学报(自然科学版), 2006, 42(2): 148-155.

  • 俞扬,周志华. 集成学习中完全随机学习策略的研究. 计算机工程, 2006, 32(17): 100-102. (Image获CCML’06优秀学生论文奖)

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2005

  • Z.-H. Zhou. Comprehensibility of data mining algorithms. In: J. Wang ed. Encyclopedia of Data Warehousing and Mining, Hershey, PA: IGI, 2005, 190-195.

  • Z.-H. Zhou. Machine learning in China. Asian Journal of Information Technology, 2005, 4(3): 1. this article was the editorial to the special issue Machine Learning in China: Selected Papers of CCML'04 edited by Z.-H. Zhou

  • Z.-H. Zhou, M. Li. Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(11): 1529-1541.

  • X. Tan, S. Chen, Z.-H. Zhou, F. Zhang. Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft kNN ensemble. IEEE Transactions on Neural Networks, 2005, 16(4): 875-886.

  • Z.-H. Zhou, Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, 35(4): 725-735.

  • X. Geng, D.-C. Zhan, Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, 35(6): 1098-1107.

  • Z.-H. Zhou, K. Jiang, M. Li. Multi-instance learning based web mining. Applied Intelligence, 2005, 22(2): 135-147.

  • S. Chen, L. Chen, Z.-H. Zhou. A unified SWSI-KAMs framework and performance evaluation on face recognition. Neurocomputing, 2005, 68: 54-69.

  • D. Zhang, Z.-H. Zhou. (2D)2PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition. Neurocomputing, 2005, 69(1-3): 224-231.

  • D. Zhang, S. Chen, Z.-H. Zhou. A new face recognition method based on SVD perturbation for single example image per person. Applied Mathematics and Computation, 2005, 163(2): 895-907.

  • Z.-H. Zhou, Y. Yu. Adapt bagging to nearest neighbor classifiers. Journal of Computer Science & Technology, 2005, 20(1): 48-54. Invited paper

  • Z.-H. Zhou, M. Li. Semi-supervised regression with co-training. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05), Edinburgh, Scotland, 2005, pp.908-913.

  • M. Li, Z.-H. Zhou. SETRED: self-training with editing. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'05), Hanoi, Vietnam, LNAI 3518, 2005, pp.611-621.

  • X. Tan, S. Chen, Z.-H. Zhou, F. Zhang. Feature selection for high dimensional face image using self-organizing maps. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'05), Hanoi, Vietnam, LNAI 3518, 2005, pp.500-504.

  • Z.-H. Zhou, X.-B. Xue, Y. Jiang. Locating regions of interest in CBIR with multi-instance learning techniques. In: Proceedings of the 18th Australian Joint Conference on Artificial Intelligence (AJCAI'05), Sydney, Australia, LNAI 3809, 2005, pp.92-101.

  • Y. Yang, S. Chen, Z.-H. Zhou, H. Lin, Y. Ye. An intelligent medical image understanding method using two-tier neural network ensembles . In: Proceedings of the 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE'05), Bari, Italy, LNAI 3533, 2005, pp.616-618.

  • Y. Jiang, J.-J. Ling, G. Li, H. Dai, Z.-H. Zhou. Dependency bagging. In: Proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC'05), Regina, Canada, LNAI 3641, 2005, pp.491-500.

  • M.-L. Zhang, Z.-H. Zhou. A k-nearest neighbor based algorithm for multi-label classification. In: Proceedings of the 1st IEEE International Conference on Granular Computing (GrC'05), Beijing, China, 2005, pp.718-721.

  • N. Li, H.-J. Zhou, J.-J. Ling, Z.-H. Zhou. Spiculated lesion detection in digital mammogram based on artificial neural network ensemble. In: Proceedings of the 2nd International Symposium on Neural Networks (ISNN'05), Chongqing, China, LNCS 3498, 2005, pp.790-795.

  • D. Zhang, S. Chen, Z.-H. Zhou. Two-dimensional non-negative matrix factorization for face representation and recognition. In: Proceedings of the 2nd International Workshop on Analysis and Modeling of Faces and Gestures (AMFG'05), in Conjunction with ICCV'05, Beijing, China, LNCS 3723, 2005, pp.350-363.

  • 何力, 张军平, 周志华. 基于放大因子和延伸方向研究流形学习算法. 计算机学报, 2005, 28(12): 2000-2009.

  • 唐伟, 周志华. 基于Bagging的选择性聚类集成. 软件学报, 2005, 16(4): 496-502.

  • 詹德川, 周志华. 基于集成的流形学习可视化. 计算机研究与发展, 2005, 18(4): 480-485. (Image获第1届中国分类技术及应用研讨会优秀学生论文奖)

  • 刘胥影, 周志华. 代价敏感的k近邻方法. 计算机研究与发展, 2005, 42(增刊B): 23-27. (第1届中国分类技术及应用研讨会)

  • 黎铭, 周志华. 局部割边权统计量的正态分布近似对SETRED算法的影响. 计算机研究与发展, 2005, 42(增刊B): 33-37. (第1届中国分类技术及应用研讨会)

  • 张道强, 陈松灿, 周志华. 核双向联想记忆框架及鲁棒人脸识别. 计算机研究与发展, 2005, 42(增刊B): 184-188. (第1届中国分类技术及应用研讨会)

  • 陈可佳, 姜远, 周志华. 基于主动相关反馈的图像检索方法. 模式识别与人工智能, 2005, 18(4): 480-485.

  • 闫明松, 周志华. 代价敏感分类算法的实验比较. 模式识别与人工智能, 2005, 18(5): 628-635.

  • 刘胥影, 周志华. 处理多类问题的代价敏感支持向量机. 江苏工业学院学报, 2005, 17(4): 1-4. (2005年江苏省人工智能会议)

  • 张宇, 周志华. 影响性别分类的人脸特征研究. 江苏工业学院学报, 2005, 17(4): 9-12. (2005年江苏省人工智能会议)

  • 周志华. 机器学习研究进展. 中国人工智能学会第11届全国学术年会论文集, 北京: 北京邮电大学出版社, 2005, 41. (特邀报告摘要)

  • 薛晓冰, 韩洁凌, 姜远, 周志华. 一种Web目录页面链接推荐的方法. 中国人工智能学会第11届全国学术年会论文集, 北京: 北京邮电大学出版社, 2005, 406-411. (Image获大会优秀论文奖)

  • 耿新, 周志华. 基于选择性多本征空间集成的人脸识别. 卢汉清主编, 计算机视觉与目标识别进展, 北京: 知识产权出版社, 2005, 135-141.

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2004

  • Z.-H. Zhou and R. Lu. Artificial intelligence in medicine in China. Artificial Intelligence in Medicine, 2004, 32(1): 1-2. this article was the editorial to the special issue Artificial Intelligence in Medicine in China edited by Z.-H. Zhou and R. Lu

  • Z.-H. Zhou and Y. Jiang. NeC4.5: neural ensemble based C4.5. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(6): 770-773.

  • Z.-H. Zhou and X. Geng. Projection functions for eye detection. Pattern Recognition, 2004, 37(5): 1049-1056.

  • S. Chen, J. Liu, and Z.-H. Zhou. Making FLDA applicable to face recognition with one sample per person. Pattern Recognition, 2004, 37(7): 1553-1555.

  • M.-L. Zhang and Z.-H. Zhou. Improve multi-instance neural networks through feature selection. Neural Processing Letters, 2004, 19(1): 1-10.

  • Y. Jiang and Z.-H. Zhou. SOM ensemble-based image segmentation. Neural Processing Letters, 2004, 20(3): 171-178.

  • Z.-H. Zhou. Rule extraction: using neural networks or for neural networks? Journal of Computer Science and Technology, 2004, 19(2): 249-253.

  • Z.-H. Zhou, K.-J. Chen, and Y. Jiang. Exploiting unlabeled data in content-based image retrieval. In: Proceedings of the 15th European Conference on Machine Learning (ECML'04), Pisa, Italy, LNAI 3201, 2004, pp.525-536.

  • Z.-H. Zhou, D. Wei, G. Li, and H. Dai. On the size of training set and the benefit from ensemble. In: Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'04), Sydney, Australia, LNAI 3056, 2004, pp.298-307.

  • H. Dai, G. Li, and Z.-H. Zhou. Ensembling MML causal discovery. In: Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'04), Sydney, Australia, LNAI 3056, 2004, pp.260-271.

  • Y. Gao, J. Z. Huang, H. Rong, and Z.-H. Zhou. Meta-game equilibrium for multi-agent reinforcement learning. In: Proceedings of the 17th Australian Joint Conference on Artificial Intelligence (AJCAI'04), Cairns, Australia, LNAI 3339, 2004, pp.930-936.

  • J. Zhang, L. He, and Z.-H. Zhou. Analyzing magnification factors and principal spread directions in manifold learning. In: Proceedings of the 9th Online World Conference on Soft Computing in Industrial Applications (WSC9), 2004, pp.xxx-xxx.

  • D. Zhang, S. Chen, and Z.-H. Zhou. Fuzzy-kernel learning vector quantization. In: Proceedings of the 1st International Symposium on Neural Networks (ISNN'04), Dalian, China, LNCS 3173, 2004, pp.180-185.

  • Y. Jiang and Z.-H. Zhou. Editing training data for kNN classifiers with neural network ensemble. In: Proceedings of the 1st International Symposium on Neural Networks (ISNN'04), Dalian, China, LNCS 3173, 2004, pp.356-361.

  • J. Liu, S. Chen, and Z.-H. Zhou. Progressive principal component analysis. In: Proceedings of the 1st International Symposium on Neural Networks (ISNN'04), Dalian, China, LNCS 3173, 2004, pp.768-773.

  • X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang. Robust face recognition from a single training image per person with kernel-based SOM-face. In: Proceedings of the 1st International Symposium on Neural Networks (ISNN'04), Dalian, China, LNCS 3173, 2004, pp.858-863.

  • J. Zhang, H. Shen, and Z.-H. Zhou. Unified locally linear embedding and linear discriminant analysis algorithm (ULLELDA) for face recognition. In: Proceedings of the 5th Chinese Conference on Biometric Recognition (Sinobiometrics'04), Guangzhou, China, LNCS 3338, 2004, pp.299-307.

  • M.-L. Zhang and Z.-H. Zhou. Ensembles of multi-instance neural networks. In: Proceedings of the International Conference on Intelligent Information Processing (ICIIP'04), Beijing, China, 2004, pp.471-474.

  • 周志华, 曹存根 主编. 神经网络及其应用, 北京: 清华大学出版社, 2004. (ISBN 7-302-08650-8 / TP.6203)

  • 周志华. 神经网络规则抽取. 见: 周志华, 曹存根 主编, 神经网络及其应用, 北京: 清华大学出版社, 2004, 321-342 (第10章).

  • 黎铭, 薛晓冰, 周志华. 基于多示例学习的中文Web目录页面推荐.软件学报, 2004, 15(9): 1328-1335.

  • 凌锦江, 周志华. 基于因果发现的神经网络集成方法. 软件学报, 2004, 15(10): 1479-1484.

  • 陈可佳, 姜远, 周志华. 主动相关反馈.第9届中国机器学习会议, 上海, 2004. (Image获优秀学生论文奖)

  • 凌锦江, 周志华. 基于特征选择的神经网络的集成方法. 复旦大学学报, 2004, 43(5): 685-688. (第9届中国机器学习会议, CCML'04, 上海)

  • 黎铭, 周志华. Fretcit-kNN算法性能分析. 计算机科学, 2004, 31(10.A): 152-155.(第4届中国Rough集与软计算会议, CRSSC’04, 舟山)

  • 刘胥影, 陈可佳, 周志华. 软计算Agent系统综述. 计算机科学, 2004, 31(10.A): 249-251.(第4届中国Rough集与软计算会议, CRSSC’04,舟山)

  • 耿新, 周志华. 基于选择性多本征空间集成的人脸识别. 目标识别与计算机视觉研讨会, 北京, 2004.

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2003

  • Z.-H. Zhou. Three perspectives of data mining. Artificial Intelligence, 2003, 143(1):139-146.

  • Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, 7(1): 37-42. Z.-H. Zhou, Y. Jiang, and S.-F. Chen. Extracting symbolic rules from trained neural network ensembles. AI Communications, 2003, 16(1): 3-15.

  • J. Wu and Z.-H. Zhou. Efficient face candidates selector for face detection. Pattern Recognition, 2003, 36(5): 1175-1186.

  • Z.-H. Zhou and S.-F. Chen. Evolving fault-tolerant neural networks. Neural Computing and Applications, 2003, 11(3-4): 156-160.

  • Z.-H. Zhou and X.-H. Shen. Virtual creatures controlled by developmental and evolutionary CPM neural networks. Intelligent Automation and Soft Computing, 2003, 9(1): 23-30.

  • Z.-H. Zhou and M.-L. Zhang. Ensembles of multi-instance learners. In: Proceedings of the 14th European Conference on Machine Learning (ECML'03), Cavtat-Dubrovnik, Croatia, LNAI 2837, 2003, pp.492-502.

  • Z.-H. Zhou, M.-L. Zhang, and K.-J. Chen. A novel bag generator for image database retrieval with multi-instance learning techniques. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), Sacramento, CA, 2003, pp.565-569.

  • Z.-H. Zhou and W. Tang. Selective ensemble of decision trees. In: Proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC'03), Chongqing, China, LNAI 2639, 2003, pp.476-483.

  • Y. Jiang, K.-J. Chen, and Z.-H. Zhou. SOM-based image segmentation. In: Proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC'03), Chongqing, China, LNAI 2639, 2003, pp.640-643.

  • Y. Jiang, Z.-H. Zhou, and M. U. Chowdhury. Image retrieval based on the combination of color and keyword. In: Proceedings of the International Conference on Computer Science, Software Engineering, Information Technology, e-Business, and Applications (CSITeA'03), Rio de Janeiro, Brazil, 2003, pp.450-453.

  • 张敏灵, 周志华. 基于神经网络的多示例回归算法. 软件学报, 2003, 14(7): 1238-1242.

  • 耿新, 周志华, 陈世福. 基于混合投影函数的眼睛定位. 软件学报, 2003, 14(8): 1394-1400.

  • 姜远, 陈兆乾, 周志华. 一种基于神经网络集成的规则学习算法. 计算机研究与发展, 2003, 40(10): 1419-1423.

  • 姜远, 陈兆乾, 周志华. 一种改进的决策规则生成算法. 广西师范大学学报, 2003, 21(1): 83-86.

  • 陈可佳, 姜远, 周志华. 基于SOM神经网络的颜色图像分割. 计算机科学, 2003, 30(4): 171-173.

  • 张敏灵, 周志华. 混合型多示例学习算法. 中国人工智能学会第10届全国学术年会论文集, 北京: 北京邮电大学出版社, 2003, 414-417.(Image优秀论文奖)

  • 黎铭, 周志华. 基于多示例学习的Web目录网页推荐系统. 中国人工智能学会第10届全国学术年会论文集, 北京: 北京邮电大学出版社, 2003, 340-343.

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