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SVM—Introductory Literature

  • “Statistical Learning Theory” by Vapnik: extremely hard to understand, containing many errors too.
  • C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2), 1998.
    • Better than the Vapnik’s book, but still written too hard for introduction, and the examples are so not-intuitive
  • The book “An Introduction to Support Vector Machines” by N. Cristianini and J. Shawe-Taylor
    • Also written hard for introduction, but the explanation about the mercer’s theorem is better than above literatures
  • The neural network book by Haykins
    • Contains one nice chapter of SVM introduction

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