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MIKI

Description: The package includes the MATLAB code of the MIKI (Multi-Instance Learning with Key Instance Shift) algorithm which focuses on handling the setting when Multi-instance learning encounters with key (positive) instance shift [1]. You will find an example of using this code in the 'example.m' function. The example data is 20newsgroup dataset comp_gra_ibm.

Reference:

[1] Y.-L. Zhang and Z.-H. Zhou. Multi-Instance Learning with Key Instance Shift. In: Proceeding of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (zhouzh@nju.edu.cn).

Requirement: The package was developed with MATLAB(R2014b). To use this package, the Matlab version of LibSVM [2] and uLSIF [3] must be available (which have been already included in /lib).

Refer:

[2] C.-C. Chang and C.-J. Lin. LIBSVM: a Library for Support Vector Machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 27.

[3] T. Kanamori, S. Hido and M. Sugiyama. A Least-squares Approach to Direct Importance Estimation[J]. Journal of Machine Learning Research, 2009, 10(Jul): 1391-1445.

ATTN2: This package was developed by Mr. Ya-Lin Zhang (zhangyl@lamda.nju.edu.cn). For any problem concerning the code, please feel free to contact Mr. Zhang.

Download: [code] (872KB)
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