Support Vector Machine

• Basics:
  • SVM: the Maximum Margin Classifer - Introduction to Support Vector Machine.
  • SVM: Support the Vector - Introduction SVM objective function of the dual optimization derivation, and draw the concept of "support vector" of.
  • SVM: Kernel - introduction of nuclear methods and thus the SVM extended to nonlinear case.
  • SVM: Outliers - describes the use of support vector machines slack variable processing method outliers.
  • SVM: Numerical Optimization - a brief introduction of SVM to solve solving numerical optimization algorithm.
• Side story:
  • SVM: Duality - some additional theories about the derivation of the dual problem.
  • SVM: Kernel II - some theoretical nuclear complementary methods, Profile Reproducing Kernel Hilbert Space and Representer Theorem of.
  • SVM: Regression - Introduction on how to use SVM to do Regression of.