An effective incremental algorithm for ν-support vector machine

Authors:
Bin Gu;Jian-Dong Wang;Guan-Sheng Zheng;Tao Li
Affiliations:
Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P.R. China;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P.R. China;College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, P.R. China;College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, P.R. China
Venue:
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Year:
2009

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Abstract

The ν-Support Vector Machine (ν-SVM) for classification has the advantage of using a parameter ν on controlling the number of support vectors. However, comparing to regular C-SVM, its formulation is more complicated because of having an additional inequality so up to now there are no exact and effective methods for incremental ν-SVM learning. In this paper, based on the truth that the additional inequality can be treated as an equality, we propose an effective and exact incremental learning algorithm for ν-SVM which conquers the difficult problem the incremental learning path may break off by the original incremental method for C-SVM.