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An Introduction to Kernel-Based Learning Algorithms




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 applications such as optical character recognition and DNA analysis

Author(s): Müller, K-R. and Mika, S. and Rätsch, G. and Tsuda, K. and Schölkopf, B.
Journal: IEEE Transactions on Neural Networks
Volume: 12
Number (issue): 2
Pages: 181-201
Year: 2001
Month: March
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1109/72.914517
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik


  title = {An Introduction to Kernel-Based Learning Algorithms},
  author = {M{\"u}ller, K-R. and Mika, S. and R{\"a}tsch, G. and Tsuda, K. and Sch{\"o}lkopf, B.},
  journal = {IEEE Transactions on Neural Networks},
  volume = {12},
  number = {2},
  pages = {181-201},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = mar,
  year = {2001},
  month_numeric = {3}