: The package includes the MATLAB code of the multi-instance learning algorithms miVLAD and miFV, which are efficient and scalable MIL algorithms. miVLAD/miFV maps the original MIL bags into a new feature vector representation, which can obtain bag-level information, and meanwhile lead to excellent performances even with linear classifiers. In consequence, thanks to the low computational cost in the mapping step and the scalability of linear classifiers, miVLAD/miFV can handle large scale MIL data efficiently and effectively. A Readme file and some data files are included in the package.References
1. X.-S. Wei, J. Wu and Z.-H. Zhou. Scalable Algorithms for Multi-Instance Learning. IEEE Transactions on Neural Networks and Learning Systems, in press.
2. X.-S. Wei, J. Wu and Z.-H. Zhou. Scalable Multi-Instance Learning. In: Proceedings of the 14th International Conference on Data Mining (ICDM’14), Shenzhen, China, 2014, pp.1037-1042.ATTN
: This packages are free for academic usage. You can run them at your own risk. For other purposes, please contact Prof. Jianxin Wu (firstname.lastname@example.org).Requirement
: To use this package, the Matlab version of Liblinear (http://www.csie.ntu.edu.tw/~cjlin/liblinear/) and the Matlab version of VLFeat (http://www.vlfeat.org/) must be available.Refer:
1. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “LIBLINEAR: A Library for Large Linear Classification,” J. Machine Learning Research, vol. 9, pp. 1871–1874, 2008.
2. A. Vedaldi and B. Fulkerson, VLFeat: An Open and Portable Library of Computer Vision Algorithms, 2008ATTN2
: This packages were developed by Mr. Xiu-Shen Wei (email@example.com). For any problem concerning the code, please feel free to contact Mr. Wei.Download