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论著

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

2018

[Conference Paper][Journal Article]

Conference Paper
  • W.-Z. Dai and Z.-H. Zhou. Combining logic abduction and statistical induction: Discovering written primitives with human knowledge. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

  • J. Feng and Z.-H. Zhou. AutoEncoder by forest. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • T. Zhang and Z.-H. Zhou. Optimal margin distribution clustering. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • P. Zhao and Z.-H. Zhou. Label distribution learning by optimal transport. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • H.-C. Dong, Y.-F. Li, and Z.-H. Zhou. Learning from semi-supervised weak-label data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • C. Liu, P. Zhao, S.-J. Huang, Y. Jiang, and Z.-H. Zhou. Dual set multi-label learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • H. Wang, H. Qian, and Y. Yu. Noisy derivative-free optimization with value suppression. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18) , New Orleans, LA, 2018.

  • Z. Xie and M. Li. Semi-supervised AUC optimization without guessing labels of unlabeled data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • Q.-Y. Jiang and W.-J Li. Asymmetric Deep Supervised Hashing. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • L.-Z. Guo, Y.-F. Li. A general formulation for safely exploiting weakly supervised data. In: Proceedings of the 32nd AAAI conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • W.-Y. Lin, Y. Mi, J.-X. Wu, K.Lu and H.-K. Xiong. Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • Y. Yang, Y.-F. Wu, D.-C. Zhan, Y. Jiang. Multi-Network User Identification via Graph-Aware Embedding. In: Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'18) , Melbourne, Australia, 2018.



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Journal Article and Book
  • Y. Zhu, K. M. Ting, and Z.-H. Zhou. Multi-label learning with emerging new labels. IEEE Transactions on Knowledge and Data Engineering, in press.

  • Y. Zhu, J. Kwok, and Z.-H. Zhou. Multi-label learning with global and local correlation. IEEE Transactions on Knowledge and Data Engineering, in press.

  • C. Hou and Z.-H. Zhou. One-pass learning with incremental and decremental features. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.

  • Y. Yu, S.-Y. Chen, Q. Da, and Z.-H. Zhou. Reusable reinforcement learning via shallow trails. IEEE Transactions on Neural Networks and Learning Systems, in press.

  • Y.-X. Ding and Z.-H. Zhou. Crowdsourcing with unsure option. Machine Learning, in press.

  • C. Qian, J.-C. Shi, K. Tang, and Z.-H. Zhou. Constrained monotone k-submodular function maximization using multi-objective evolutionary algorithms with theoretical guarantee. IEEE Transactions on Evolutionary Computation, in press.

  • X.-S. Wei, C.-L. Zhang, H. Zhang, J.-X. Wu. Deep Bimodal Regression of Apparent Personality Traits from Short Video Sequences. IEEE Transactions on Affective Computing.

  • X.-S Wei, C.-W Xie, J.-X Wu, and C.-H Shen. Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recognition , 76, 2018: 704-714.

  • C. Qian, Y. Yu, and Z.-H. Zhou. Analyzing evolutionary optimization in noisy environments. Evolutionary Computation , 2018, in press.

  • C. Qian, Y. Yu, K. Tang, Y.-C Jin, X. Yao, and Z.-H. Zhou. On the effectiveness of sampling for evolutionary optimization in noisy environments. Evolutionary Computation , 2018, in press.

  • T. Sun and Z.-H. Zhou. Structural diversity of decision tree ensemble learning. Frontiers of Computer Science, in press.

  • Z.-H. Zhou. A brief introduction to weakly supervised learning. National Science Review, 2018, 5(1): 44-53.

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