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SVM - Support Vector Machines
Books


  1. V. Vapnik and A. Chervonenkis, Theory of Pattern Recognition, Nauka, Moscow, 1974.
  2. V. Vapnik, Estimation of Dependencies Based on Empirical Data, Nauka, Moscow, 1979.
  3. V. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995.
  4. V. Vapnik, Statistical Learning Theory, Wiley-Interscience, New York, 1998.
  5. B. Schölkopf, C. J. C. Burges, and A. J. Smola, Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, 1999.
  6. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, 2000.
  7. A. J. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, Advances in Large Margin Classifiers, MIT Press, Cambridge, MA, 2000.
  8. V. Kecman, Learning and Soft Computing, MIT Press, Cambridge, MA, 2001.
  9. B. Schölkopf and A. J. Smola, Learning with Kernels, MIT Press, Cambridge, MA, 2002.
  10. T. Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms, Kluwer, 2002.
  11. R. Herbrich, Learning Kernel Classifiers, MIT Press, Cambridge, MA, 2002.
  12. J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002.
  13. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge, 2004.
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