Generalizing the bias term of support vector machines

Authors:
Wenye Li;Kwong-Sak Leung;Kin-Hong Lee
Affiliations:
Department of Computer Science and Engineering, The Chinese University of Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

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Abstract

Based on the study of a generalized form of representer theorem and a specific trick in constructing kernels, a generic learning model is proposed and applied to support vector machines. An algorithm is obtained which naturally generalizes the bias term of SVM. Unlike the solution of standard SVM which consists of a linear expansion of kernel functions and a bias term, the generalized algorithm maps predefined features onto a Hilbert space as well and takes them into special consideration by leaving part of the space unregularized when seeking a solution in the space. Empirical evaluations have confirmed the effectiveness from the generalization in classification tasks.