Chapter 2. An Introduction to Kernel Learning Algorithms

  1. Dr Gustavo Camps-Valls B.Sc., Ph.D. professor member2 and
  2. Dr Lorenzo Bruzzone M.S., Ph.D. Postdoctoral Researcher Professor member Chair3
  1. Peter V. Gehler and
  2. Bernhard Schölkopf

Published Online: 4 NOV 2009

DOI: 10.1002/9780470748992.ch2

Kernel Methods for Remote Sensing Data Analysis

Kernel Methods for Remote Sensing Data Analysis

How to Cite

Gehler, P. V. and Schölkopf, B. (2009) An Introduction to Kernel Learning Algorithms, in Kernel Methods for Remote Sensing Data Analysis (eds G. Camps-Valls and L. Bruzzone), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470748992.ch2

Editor Information

  1. 2

    Image Processing Laboratory (IPL) & Dept. Enginyeria Electrónica, Universitat de Valéncia, Spain

  2. 3

    Dept. Information Engineering and Computer Science, University of Trento, Italy

Author Information

  1. Max Planck Institute for Biological Cybernetics, Germany

Publication History

  1. Published Online: 4 NOV 2009
  2. Published Print: 23 OCT 2009

ISBN Information

Print ISBN: 9780470722114

Online ISBN: 9780470748992

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Keywords:

  • kernel learning algorithms - standard tool in area of machine learning and pattern recognition;
  • measuring similarity with kernels;
  • reproducing kernel Hilbert space (RKHS) and reproducing property of kernel;
  • operations in RKHS;
  • kernel construction;
  • homogeneous polynomial kernel;
  • spatial kernel for image processing;
  • representer theorem;
  • learning with kernels;
  • kernel principal component analysis (PCA)

Summary

This chapter contains sections titled:

  • Introduction

  • Kernels

  • The representer theorem

  • Learning with kernels

  • Conclusion

  • References