Difference between revisions of "10-601 Ensembles 2"
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=== Summary === | === Summary === | ||
| − | You should understand the basic intuitions behind the analysis of 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 reducing an upper bound on error and hence fitting the training data. |
| − | * As a coordinate descent optimization of the same upper bound | + | * As a coordinate descent optimization of the same upper bound. |
| − | You should also be aware that boosting is related to margin classifiers | + | You should also be aware that boosting is related to margin classifiers. |
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.
