Difference between revisions of "10-601 Ensembles 2"
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* [http://dl.acm.org/citation.cfm?id=743935 Ensemble Methods in Machine Learning], Tom Dietterich | * [http://dl.acm.org/citation.cfm?id=743935 Ensemble Methods in Machine Learning], Tom Dietterich | ||
* [http://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf A Short Introduction to Boosting], Yoav Freund and Robert Schapire. | * [http://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf A Short Introduction to Boosting], Yoav Freund and Robert Schapire. | ||
| − | * Optional: [http://dl.acm.org/citation.cfm?id=279960 Improved boosting algorithms using confidence-rated predictions], Robert Schapire and Yoram Singer. | + | * Optional: [http://dl.acm.org/citation.cfm?id=279960 Improved boosting algorithms using confidence-rated predictions], Robert Schapire and Yoram Singer. (This paper has the analysis that I presented in class.) |
=== Summary === | === Summary === | ||
Revision as of 10:53, 17 October 2013
Slides
- Slides in PowerPoint.
- Margin "movie" I showed in class: Margin movie.
Readings
- Ensemble Methods in Machine Learning, Tom Dietterich
- A Short Introduction to Boosting, Yoav Freund and Robert Schapire.
- Optional: Improved boosting algorithms using confidence-rated predictions, Robert Schapire and Yoram Singer. (This paper has the analysis that I presented in class.)
Summary
You should understand the basic intuitions behind the analysis of boosting
- As reducing an upper bound on error and hence fitting the training data
- As a coordinate descent optimization of the same upper bound
You should also be aware that boosting is related to margin classifiers
