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Notes about SVM—Introductory Literature

  • “Statistical Learning Theory” by Vapnik: difficult to understand, containing many errors.
  • C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining, 2(2), 1998.
    • Easier than Vapnik’s book, but still not introductory level; the examples are not so intuitive
  • The book An Introduction to Support Vector Machines by Cristianini and Shawe-Taylor
    • Not introductory level, but the explanation about Mercer’s Theorem is better than above literatures
  • Neural Networks and Learning Machines by Haykin
    • Contains a nice chapter on SVM introduction

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