Difference between revisions of "10-601 Ensembles 2"
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| − | This a lecture used in the [[Syllabus for Machine Learning 10-601]] | + | This a lecture used in the [[Syllabus for Machine Learning 10-601 in Fall 2014]] |
=== Slides === | === Slides === | ||
Latest revision as of 17:36, 21 July 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
- Slides in PowerPoint.
- Margin "movie" I showed in class: Margin movie.
- I also did a demo of Weka. There's a presentation on the weka GUIs which covers some of the same material.
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.
