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SFL

Description: The package includes the MATLAB code of the SFL (Storage Fit Learning with unlabeled data) which focuses on the graph-based semi-supervised learning and includes two storage fit learning approaches NysCK and SoCK, which can adjust their behaviors to different storage budgets.[1]. You will find four main processes whose names include 'main' in which NysCK and SoCK are invoked seperately in two settings. The two example datasets are adult-a and australian which lie in './data/LargeScale/' and './data/RegularScale/' respectivly.

Reference:

[1] B.-J. Hou, L. Zhang, and Z.-H. Zhou. Storage fit learning with unlabeled data. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

[2] C. C. Chang, C. J. Lin. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2(3): 27.

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).

ATTN2: This package was developed by Mr. Bo-Jian Hou (houbj@lamda.nju.edu.cn). For any problem concerning the code, please feel free to contact Mr. Hou.

Download: code (1790KB)
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