Training support vector machines using greedy stagewise algorithm

Authors:
Liefeng Bo;Ling Wang;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, China
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

Hard margin support vector machines (HM-SVMs) have a risk of getting overfitting in the presence of the noise. Soft margin SVMs deal with this problem by the introduction of the capacity control term and obtain the state of the art performance. However, this disposal leads to a relatively high computational cost. In this paper, an alternative method, greedy stagewise algorithm, named GS-SVMs is presented to deal with the overfitting of HM-SVMs without the introduction of capacity control term. The most attractive property of GS-SVMs is that its computational complexity scales quadratically with the size of training samples in the worst case. Extensive empirical comparisons confirm the feasibility and validity GS-SVMs.