By Robert E. Schapire
Boosting is an method of computing device studying in response to the belief of making a hugely actual predictor by way of combining many vulnerable and misguided "rules of thumb." A remarkably wealthy thought has developed round boosting, with connections to more than a few subject matters, together with records, online game conception, convex optimization, and knowledge geometry. Boosting algorithms have additionally loved functional good fortune in such fields as biology, imaginative and prescient, and speech processing. At quite a few occasions in its heritage, boosting has been perceived as mysterious, debatable, even paradoxical.This ebook, written by means of the inventors of the strategy, brings jointly, organizes, simplifies, and considerably extends twenty years of study on boosting, proposing either idea and purposes in a fashion that's available to readers from assorted backgrounds whereas additionally supplying an authoritative reference for complex researchers. With its introductory remedy of all fabric and its inclusion of routines in each bankruptcy, the e-book is acceptable for direction use in addition. The e-book starts off with a normal advent to desktop studying algorithms and their research; then explores the middle idea of boosting, specially its skill to generalize; examines a few of the myriad different theoretical viewpoints that aid to provide an explanation for and comprehend boosting; offers functional extensions of boosting for extra complicated studying difficulties; and eventually offers a couple of complicated theoretical subject matters. a variety of functions and sensible illustrations are provided all through.
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Extra resources for Boosting: Foundations and Algorithms
66]. 3 was created by Frey and Slate . 3 is due to Quinlan , and is similar to the CART algorithm of Breiman et al. . Drucker and Cortes  and Jackson and Craven  were the first to test AdaBoost experimentally. 4 were adapted. AdaBoost’s resistance to overfitting was noticed early on by Drucker and Cortes , as well as by Breiman  and Quinlan . 7, are taken from Schapire et al. . There have been numerous other systematic experimental studies of AdaBoost, such as [15, 68, 162, 209], as well as Caruana and NiculescuMizil’s  large-scale comparison of several learning algorithms, including AdaBoost.
This would appear to be a paradox. One superficially plausible explanation is that the αt ’s are converging rapidly to zero, so that the number of base classifiers being combined is effectively bounded. However, as noted above, the t ’s remain around 5–6% in this case, well below 12 , which means that the weights αt on the individual base classifiers are also bounded well above zero, so that the combined classifier is constantly growing and evolving with each round of boosting. 3, boosting certainly can overfit.
1, AdaBoost is intended for the simplest learning setting in which the goal is binary classification, that is, classification problems with only two possible classes or categories. To apply AdaBoost to a much broader range of real-world learning problems, the algorithm must be extended along multiple dimensions. In chapter 9, we describe an extension to AdaBoost in which the base classifiers themselves are permitted to output predictions that vary in their self-rated level of confidence. In practical terms, this modification of boosting leads to a dramatic speedup in learning time.