Difference between revisions of "10-601 Ensembles"
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| − | + | * Ziv's lecture: [http://www.cs.cmu.edu/~zivbj/classF14/boosting.pdf Slides in pdf]. | |
* [http://www.cs.cmu.edu/~wcohen/10-601/ensembles.ppt Slides in PowerPoint], [http://www.cs.cmu.edu/~wcohen/10-601/ensembles.pdf Slides in PDF]. | * [http://www.cs.cmu.edu/~wcohen/10-601/ensembles.ppt Slides in PowerPoint], [http://www.cs.cmu.edu/~wcohen/10-601/ensembles.pdf Slides in PDF]. | ||
* Margin "movie" I showed in class: [http://www.cs.cmu.edu/~wcohen/10-601/margin-movie.pdf Margin movie]. | * Margin "movie" I showed in class: [http://www.cs.cmu.edu/~wcohen/10-601/margin-movie.pdf Margin movie]. | ||
Revision as of 08:27, 27 October 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
- Ziv's lecture: Slides in pdf.
- Slides in PowerPoint, Slides in PDF.
- 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 how these ensemble methods work
- Bagging
- Stacking
- Boosting
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.
