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Introduction

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Support Vector Machines for Pattern Classification

Part of the book series: Advances in Pattern Recognition ((ACVPR))

Abstract

Support vector machines and their variants and extensions, often called kernel-based methods (or simply kernel methods), have been studied extensively and applied to various pattern classification and function approximation problems. Pattern classification is to classify some object into one of the given categories called classes.

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Notes

  1. 1.

    We may define the sign function by

    $\textrm{sign}(x)=\begin{cases}1 &x > 0,\\ 0 &x=0,\\-1 &x < 0.\end{cases}$
  2. 2.

    It is my regret that I could not reevaluate the computer experiments, included in the book, that violate this rule.

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  1. Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, 657-8501, Japan

    Prof. Dr. Shigeo Abe

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Abe, S. (2010). Introduction. In: Support Vector Machines for Pattern Classification. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84996-098-4_1

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  • DOI: https://doi.org/10.1007/978-1-84996-098-4_1

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