Introduction to Kernel Methods

Posted in Science on July 28, 2008


Introduction to Kernel Methods

Lecture slides:

  • Kernel-based algorithms
  • Regression/Classification
  • Example of regression
  • Regularization
  • RKHS as smoothness penalty
  • Kernel classification/regression
  • Representer theorem
  • Reproducing property
  • Proof of representer theorem
  • Algorithms: RLS
  • RLS demo
  • Algorithms: RLS_
  • Support Vector Machines
  • Support Vector Machines: Sparsity
  • Feature map interpretation
  • Feature map: RLS
  • Generalization error
  • Generalization bound
  • Some References

Author: Mikhail Belkin, Ohio State University

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Tags: Science, Lectures, Computer Science, Machine Learning, VideoLectures.Net, Kernel Methods