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Kernel Methods in Machine Learning




We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data.

Author(s): Hofmann, T. and Schölkopf, B. and Smola, AJ.
Journal: Annals of Statistics
Volume: 36
Number (issue): 3
Pages: 1171-1220
Year: 2008
Month: June
Day: 0

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

Digital: 0
DOI: 10.1214/009053607000000677
Institution: Max Planck Institute for Biological Cybernetics, Tübingen
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Kernel Methods in Machine Learning},
  author = {Hofmann, T. and Sch{\"o}lkopf, B. and Smola, AJ.},
  journal = {Annals of Statistics},
  volume = {36},
  number = {3},
  pages = {1171-1220},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, Tübingen},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2008},
  month_numeric = {6}