kernel SVM

(30 minutes to learn)


The main advantage of the SVM as a linear classifier is that it can be kernelized in order to represent complex nonlinear decision boundaries. Conveniently, since only a (hopefully) sparse subset of the training examples are used, kernels only need to be computed with a small fraction of the training examples. Kernel SVMs are one of the most widely used classifiers in machine learning, because off-the-shelf tools often perform very well.


This concept has the prerequisites:

Core resources (read/watch one of the following)


The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
Authors: Trevor Hastie,Robert Tibshirani,Jerome Friedman

Supplemental resources (the following are optional, but you may find them useful)


Coursera: Machine Learning (2013)
An online machine learning course aimed at a broad audience.
Author: Andrew Y. Ng
Other notes:
  • Click on "Preview" to see the videos.


See also