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An introduction to Kernel Learning Algorithms


Book Chapter



Kernel learning algorithms are currently becoming a standard tool in the area of machine learning and pattern recognition. In this chapter we review the fundamental theory of kernel learning. As the basic building block we introduce the kernel function, which provides an elegant and general way to compare possibly very complex objects. We then review the concept of a reproducing kernel Hilbert space and state the representer theorem. Finally we give an overview of the most prominent algorithms, which are support vector classification and regression, Gaussian Processes and kernel principal analysis. With multiple kernel learning and structured output prediction we also introduce some more recent advancements in the field.

Author(s): Peter Gehler and Bernhard Schölkopf
Book Title: Kernel Methods for Remote Sensing Data Analysis
Pages: 25--48
Year: 2009
Editors: Gustavo Camps-Valls and Lorenzo Bruzzone
Publisher: Wiley

Department(s): Empirical Inference, Perceiving Systems
Bibtex Type: Book Chapter (inbook)
Paper Type: Book Chapter

Address: New York, NY, USA
Chapter: 2
DOI: 10.1002/9780470748992.ch2
URL: http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470722118.html


  title = {An introduction to Kernel Learning Algorithms},
  author = {Gehler, Peter and Sch{\"o}lkopf, Bernhard},
  booktitle = {Kernel Methods for Remote Sensing Data Analysis},
  pages = {25--48},
  chapter = {2},
  editors = {Gustavo Camps-Valls and Lorenzo Bruzzone},
  publisher = {Wiley},
  address = {New York, NY, USA},
  year = {2009},
  url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470722118.html}