A new dual ν-support vector machine

Authors:
Jia Yinshan;Wang Yumei
Affiliations:
School of Information Technology, Liaoning University of Petroleum and Chemical Technology, Fushun, China;Computing Center, Qingdao Port Co, Ltd., Qingdao, China
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

Citing 4
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Machine Learning
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Neural Computation
Dual /spl nu/-support vector machine with error rate and training size biasing

ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02

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Abstract

ν-Support Vector Machine is one of the most widely used support vector machines because it provides a way to control the fraction of margin errors and the fraction of support vectors. However, being biased towards the class with the more training samples prevent it from being applied to some applications in which the size of classes is uneven. Although some updated ν-SVMs have been proposed to solve this problem, there are some issues in the formulations of these updated ν-SVMs. In this paper, a new ν-SVM, Double-ν Support Vector Machine, is proposed. It introduces ν+1 and ν−1 to control the upper bound on the fraction of bound support vectors and the lower bound on the fraction of support vectors of the positive class and the negative class respectively. Moreover, it reduces the complexity of formulations by eliminating a redundant constrain in ν-SVM formulations.