Difference between revisions of "10-601 Ensembles 2"
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=== Summary === | === Summary === | ||
| − | You should | + | 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 | |
| − | |||
Revision as of 16:17, 16 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.
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
