Classification - Supplemental material

slides

Useful Links:

• Machine learning class - CMU: Look at their slides about logistic regression, Naive Bayes, SVM and decision trees.
Machine learning article - Wikipedia: Contains pointers to individual articles (like SVM), as well as usuful software resources.
• Pattern Classification (2nd edition) by Duda, Hart and Stork is a good general reference for most algorithms covered in this lecture.
• An extensive empirical comparisons of supervised learning algorithms - Rich Caruana, Alexandru Niculescu-Mizil, ICML 06.

SVMs

• Good tutorial paper: Support Vector Machines: Hype or Hallelujah? - Kristin Bennet, Colin Campbell, SIGKDD Explorations, 2,2, 2000, 1-13
• Andrew Moore's slides on SVM
• A practical guide to SVM classification - From the authors of libSVM

Decision Trees

• Tom Mitchell's slides on decision trees
• Breiman's website on Random Forests

Some applications mentioned in class

• Branch prediction using perceptron in computer architecture: paper.
• Application of random forest in language modeling