An introduction to kernel-based learning algorithms

  title={An introduction to kernel-based learning algorithms},
  author={Klaus-Robert M{\"u}ller and Sebastian Mika and Gunnar R{\"a}tsch and Koji Tsuda and Bernhard Sch{\"o}lkopf},
  journal={IEEE transactions on neural networks},
  volume={12 2},
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing… CONTINUE READING
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Vector Learning

  • B. Schölkopf, Support
  • 1997
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