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by admin last modified 2011-11-24 13:50

Books on SVMs and Other Kernel Machines

  • Ingo Steinwart and Andreas Christmann. Support Vector Machines. Springer, New York, 2008.
    Explains the principles of SVMs, including a rigorous treatment of state-of-the-art theoretical results on support vector machines. Suitable for both graduate students and researchers in statistical machine learning (602 pages, 84.95$, 64.15 EUR).
  • Vladimir Vapnik. Estimation of Dependences Based on Empirical Data. Springer Verlag, 2006, 2nd edition.
    The second edition of Vapnik's classic on learning theory, including several new chapters on the history of events and on non-inductive inference.
  • Grace Wahba. Spline Models for Observational Data. SIAM CBMS-NSF Regional Conference Series in Applied Mathematics vol. 59, Philadelphia, 1990.
    Discusses (reproducing) kernel methods in nonparametric regression. Not easy reading for machine learning researchers, but containing fundamental material about precedents of today's kernel machines (169 pages, $33.5).
  • Vladimir Vapnik. The Nature of Statistical Learning Theory. Springer, NY, 1995.
    An overview of statistical learning theory, containing no proofs, but most of the crucial theorems and milestones of learning theory. With a detailed chapter on SVMs for pattern recognition and regression (1st edition: 188 pages, $65; 2nd edition: 304 pages, $70).
  • Vladimir Vapnik. Statistical Learning Theory. Wiley, NY, 1998.
    The comprehensive treatment of statistical learning theory, including a large amount of material on SVMs (768 pages, $120).
  • Bernhard Schölkopf, Chris Burges, and Alex Smola (eds). Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge, MA, 1999.
    A collection of articles written by experts in the field. Includes an introductory tutorial, overviews of the theory of SVMs, contributions on novel algorithms, and three chapters on SVM implementations (392 pages, $53).
  • Nello Cristianini and John Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, UK, 2000.
    An introduction to SVMs which is concise yet comprehensive in its description of the theoretical foundations of large margin algorithms (189 pages, $45).
  • Alex Smola, Peter Bartlett, Bernhard Schölkopf, and Dale Schuurmans (eds). Advances in Large Margin Classifiers. MIT Press, Cambridge, MA, 2000.
    A collection of articles dealing with one of the main ideas of SVMs, large margin regularization. Contains an introduction, articles on new kernels, SVMs, and boosting algorithms (422 pages, $45).
  • Bernhard Schölkopf and Alex Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
    An introduction and overview over SVMs. A free sample of one third of the chapters (Introduction, Kernels, Loss Functions, Optimization, Learning Theory Part I, and Classification) is available on the book website. (650 pages, $60).
  • Ralf Herbrich. Learning Kernel Classifiers. MIT Press, Cambridge, MA, 2002.
    An introduction and overview over SVMs.(400 pages, $40).
  • Thorsten Joachims. Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms. Kluwer, 2002
    "Provides a detailed description of the Support Vector Machines (SVMs) approach to learning text classifiers including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification". (224 pages, $110)
  • John Shawe-Taylor and Nello Cristianini "Kernel Methods for Pattern Analysis", Cambridge University Press, 2004
    A comprehensive coverage of the field of kernel methods, with pseudocode for several algorithms and kernels, and matlab functions available online. Introductive and practical in style, a cookbook for the practitioner. Website:
This list is not comprehensive. There are further books with chapters on kernel machines, or even completely dedicated to the topic, cf. the publications page.

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