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Boosting. Theory and Practice

In the 2009 winter quarter CSE254 will be tought by Yoav Freund and will concentrate on boosting algorithms.

Time: Tue, Thu 12:30-1:50 PM (No class on Tue, Feb 24)

Room: New Location: Center Hall 206

Section: 644193

The topics covered will include:

  • Adaboost, LogitBoost and NormalBoost
  • The large-margin theory
    • Understanding and using empirical score distributions.
  • Using boosting for problems other than binary classification
    • The one-sided classification framework.
    • Multi-label classification
    • Regression
    • Detecting rare events
  • Active Learning using Boosting.
  • Resampling training examples to speed up training
  • The cascade method for optimizing the speed of the scoring function.

The emphasis in this class will be on the application of boosting to solve real-world problems. Students are expected to have a strong background in programming (Matlab, Java, Perl) and an interest in large scale data analysis.


  • --SunsernCheamanunkul 19:40, 28 January 2009 (UTC) You can find the code for RobustBoost [here]. In order to get the code, you need an account on seed machine. Please send an email to scheaman-at-ucsd if you need one.
  • --WilliamBeaver 18:17 25 January 2009 (PST) a pre-release of jboost-1.4.1 is now available here (not sourceforge). These packages are builds of sf.net snapshots taken at the time of this post (please verify the timestamp to be 1/25/09 18:17). This release contains many small bug fixes, a new nfold.py script with a slightly modified output structure (READMEs in /scripts are up to date), initial support for classifier output in python, plus things I cannot recall right now. If you have problems, post to jboost mail lists or email me. Note: this is a pre-release. The Jboost website does not reflect these changes. Thanks.

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