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Support Vector Machines for Pattern Classification pp 163–226Cite as

Variants of Support Vector Machines

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

Abstract

Since the introduction of support vector machines, numerous variants have been developed. In this chapter, we discuss some of them: least-squares support vector machines, linear programming support vector machines, sparse support vector machines, etc. We also discuss learning paradigms: incremental training, learning using privileged information, semi-supervised learning, multiple classifier systems, multiple kernel learning, and other topics: confidence level and visualization of support vector machines.

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Notes

  1. 1.

    For regular SVMs, this fact was shown in [11].

  2. 2.

    Strictly speaking this statement is wrong, because support vector machines are not linear-transformation invariant. But by cross-validation, the difference of the generalization abilities may be small.

  3. 3.

    Discussions with Prof. H. Motoda.

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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). Variants of Support Vector Machines. In: Support Vector Machines for Pattern Classification. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84996-098-4_4

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