Cross-Validation, Bootstrap, and Support Vector Machines. (27th July 2011)

• Record Type: Journal Article
• Authors: Tsujitani, Masaaki; Tanaka, Yusuke
• Other Names: Smolinski Tomasz G. Academic Editor.
• Is Part Of: Advances in artificial neural systems. (2011)
• Publication Date: 2011-07-27
• Subjects: Neural networks (Computer science) -- Periodicals; Neural networks (Computer science); Periodicals; Electronic journals; 006.32
• DOI: 10.1155/2011/302572
• Languages: English
• ISSNs: 1687-7594

Abstract

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.