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Seminars will be held in the Lecture Theatre 1 - William Gates Building, Computer Laboratory at 4.15pm
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Sean Holden

The Theory of Boosting: A Review

Some time ago, Rob Schapire obtained a remarkable result in computational learning theory: that "weak" classifiers can be "boosted" using a suitable algorithm such that a properly constructed combination of weak classifiers provides better performance than might be expected. Since this foundational result appeared applicable versions of the algorithm, and in particular the AdaBoost algorithm, have been used in many real problems, often with considerable success. In particular they have been demonstrated in many cases to exhibit a remarkable resistance to the usual "overfitting" problem in machine learning. In an attempt to explain this useful behaviour, and to better understand it, several theoretical frameworks have been proposed for the analysis of boosting algorithms, and the field is ripe for further study. This talk introduces boosting and reviews some of the theory that has appeared.
Wednesday Seminars

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