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Support Vector Machines

An Introduction to Support Vector Machines.
Cristianini, N. and Shawe-Taylor, J.
Cambridge University Press, 2000.

Kernel Methods for Pattern Analysis.
John Shawe-Taylor and Nello Cristianini.
Cambridge University Press, 2004.

Learning to Classify Text using Support Vector Machines.
Thorsten Joachims.
Kluwer Academic Publishers, 2002.

Learning with Kernels.
Bernhard Schölkopf and Alexander J. Smola.
MIT Press, 2001.

Text Categorization with Support Vector Machines: Learning with Many Relevant Features.
Thorsten Joachims.
Proceedings of ECML-98, 10th European Conference on Machine Learning 1997.

  • Chris Burges
  • John Platt
  • John Shawe-Taylor
  • Thorsten Joachims
  • Alexander J. Smola
  • Vladimir Vapnik
  • Bernhard Schölkopf

  • Kernel Methods for Images and Text (KERMIT)

  • SVM Light

Support Vector Machines are machine learning algorithms for binary classification based on recent advances in statistical learning theory. The input is mapped into a high dimensional feature space, in which a linear classifier is constructed that maximizes the margin between the classes and hence generalizes well to unseen data. Learning requires only information about the relative distances of the training instances, so it can be performed for arbitrary distance metrics (called kernels) that may be specific to the application domain. These generalized SVMs are called kernel machines.