Abstract
Boosting is one of the most powerful learning ideas introduced in the last ten 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.
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© 2001 Springer Science+Business Media New York
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Hastie, T., Friedman, J., Tibshirani, R. (2001). Boosting and Additive Trees. In: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21606-5_10
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DOI: https://doi.org/10.1007/978-0-387-21606-5_10
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4899-0519-2
Online ISBN: 978-0-387-21606-5
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