2009 | OriginalPaper | Buchkapitel
Boosting and Additive Trees
verfasst von : Trevor Hastie, Robert Tibshirani, Jerome Friedman
Erschienen in: The Elements of Statistical Learning
Verlag: Springer New York
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Boosting is one of the most powerful learning ideas introduced in the last twenty years. It was originally designed for classification problems, but as will be seen in this chapter, it can profitably be extended to regression as well. The motivation for boosting was a procedure that combines the outputs of many “weak” classifiers to produce a powerful “committee.” From this perspective boosting bears a resemblance to bagging and other committee-based approaches (Section 8.8). However we shall see that the connection is at best superficial and that boosting is fundamentally different.