Laplacian SVM for Learning from Label Proportions

Proportion-SVM has been deeply studied due to its broad application prospects, such as modeling voting behaviors and spam filtering. However, the geometric information has been widely ignored. Thus, current methods usually show sensitivity to noises. To address these problems, in this paper, we comb...

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Bibliographic Details
Published inIEEE ... International Conference on Data Mining workshops pp. 847 - 852
Main Authors Limeng Cui, Zhensong Chen, Fan Meng, Yong Shi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Data mining
Data models
Kernel
Laplace equations
Learning with label proportion; Proportion-SVM; Laplacian
Optimization
Support vector machines
Training
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Summary:Proportion-SVM has been deeply studied due to its broad application prospects, such as modeling voting behaviors and spam filtering. However, the geometric information has been widely ignored. Thus, current methods usually show sensitivity to noises. To address these problems, in this paper, we combine the proportion learning framework with Laplacian term. We exploit the advantages of Laplacian term. Extensive experiments show the effectiveness of our method.
ISSN:2375-9259
DOI:10.1109/ICDMW.2016.0125