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Advances in Artificial Neural Systems
Volume 2011 (2011), Article ID 302572, 6 pages
http://dx.doi.org/10.1155/2011/302572
Research Article

Cross-Validation, Bootstrap, and Support Vector Machines

1Division of Informatics and Computer Sciences, Graduate School of Engineering, Osaka Electro-Communication University, Osaka 572-8530, Japan
2Biometrics Department, Statistics Analysis Division, EPS Co., Ltd., 3-4-30 Miyahara, Yodogawa-ku, Osaka 532-0003, Japan

Received 2 April 2011; Accepted 7 June 2011

Academic Editor: Tomasz G. Smolinski

Copyright © 2011 Masaaki Tsujitani and Yusuke Tanaka. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper considers the applications of resampling methods to support vector machines (SVMs). We take into account the leaving-one-out cross-validation (CV) when determining the optimum tuning parameters and bootstrapping the deviance in order to summarize the measure of goodness-of-fit in SVMs. The leaving-one-out CV is also adapted in order to provide estimates of the bias of the excess error in a prediction rule constructed with training samples. We analyze the data from a mackerel-egg survey and a liver-disease study.