Cross-Validation, Bootstrap, and Support Vector Machines | Zendy

Masaaki Tsujitani | Zendy; Yusuke Tanaka | Zendy

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Cross-Validation, Bootstrap, and Support Vector Machines

Author(s) -

Masaaki Tsujitani,

Yusuke Tanaka

Publication year - 2011

Publication title -

advances in artificial neural systems

Language(s) - English

Resource type - Journals

eISSN - 1687-7608

pISSN - 1687-7594

DOI - 10.1155/2011/302572

Subject(s) - support vector machine , cross validation , computer science , bootstrapping (finance) , resampling , machine learning , artificial intelligence , data mining , statistics , pattern recognition (psychology) , econometrics , mathematics

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

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