Incremental Nonlinear Proximal Support Vector Machine

Authors:
Qiuge Liu;Qing He;Zhongzhi Shi
Affiliations:
The Key Laboratory of Intelligent Information Processing, Department of Intelligence Software, Institute of Computing Technology, Chinese Academy, of Sciences, Beijing, 100080, China and Graduate ...;The Key Laboratory of Intelligent Information Processing, Department of Intelligence Software, Institute of Computing Technology, Chinese Academy, of Sciences, Beijing, 100080, China and Graduate ...;The Key Laboratory of Intelligent Information Processing, Department of Intelligence Software, Institute of Computing Technology, Chinese Academy, of Sciences, Beijing, 100080, China and Graduate ...
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

Proximal SVM (PSVM), which is a variation of standard SVM, leads to an extremely faster and simpler algorithm for generating a linear or nonlinear classifier than classical SVM. An efficient incremental method for linear PSVM classifier has been introduced, but it can't apply to nonlinear PSVM and incremental technique is the base of online learning and large data set training. In this paper we focus on the online learning problem. We develop an incremental learning method for a new nonlinear PSVM classifier, utilizing which we can realize online learning of nonlinear PSVM classifier efficiently. Mathematical analysis and experimental results indicate that these methods can reduce computation time greatly while still hold similar accuracy.