Difference between revisions of "10-601 Ensembles 2"
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| + | This a lecture used in the [[Syllabus for Machine Learning 10-601 in Fall 2014]] | ||
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=== Slides === | === Slides === | ||
* [http://www.cs.cmu.edu/~wcohen/10-601/ensembles2.ppt Slides in PowerPoint]. | * [http://www.cs.cmu.edu/~wcohen/10-601/ensembles2.ppt Slides in PowerPoint]. | ||
* 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]. | ||
| + | |||
| + | * I also did a demo of [http://www.cs.waikato.ac.nz/ml/weka/ Weka]. There's a [http://prdownloads.sourceforge.net/weka/weka.ppt presentation on the weka GUIs] which covers some of the same material. | ||
=== Readings === | === Readings === | ||
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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 === | ||
| − | 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. |
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.
