A Fast BMU Search for Support Vector Machine

Authors:
Wataru Kasai;Yutaro Tobe;Osamu Hasegawa
Affiliations:
Department of Computer Intelligence and Systems Science,;Department of Computer Intelligence and Systems Science,;Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, Yokohama, Japan 226-8503
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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Abstract

As described in this paper, we propose a fast learning algorithm of a support vector machine (SVM). Our work is base on the Learning Vector Quantization (LVQ) and we compress the data to perform properly in the context of clustered data margin maximization. For solving the problem faster, we propose a fast Best Matching Unit (BMU) search and introduce it to the Threshold Order-Dependent (TOD) algorithm, which is one of the simplest form of LVQ. Experimental results demonstrate that our method is as accurate as the existing implementation, but it is faster in most situations. We also show the extension of the proposed learning framework for online re-training problem.