网页The boost by majority algorithm (BBM), suggested by Freund [4], has appealing optimality properties but has rarely been used in practice because it is not adaptive. In this paper we present and analyze an adaptive version of BBM which we call BrownBoost (the reason for the name will become clear shortly).
An Adaptive Version of the Boost by Majority Algorithm
网页This boosting algorithm is an adaptive version of the boost by majority algorithm and combines bounded goals of the boost by majority algorithm with the adaptivity of AdaBoost. The method used for making boost-by-majority adaptive is to consider the limit in which each of the boosting iterations makes an infinitesimally small contribution to ...
An adaptive version of the boost by majority algorithm
网页1999年7月6日 — A decisiontheoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55 (1): 119-139, August 1997. Digital Library. Google Scholar.
Optimal and Adaptive Algorithms for Online Boosting
网页2015年2月9日 — The first algorithm is an online version of boost-by-majority. By proving a matching lower bound, we show that this algorithm is essentially optimal in terms of the number of weak learners and the sample complexity needed …
网页The “boost by majority” algorithm (BBM), suggested by Freund (1995), has appealing optimality properties but has rarely been used in practice because it is not adaptive. In this paper we present and analyze an adaptive version of BBM which we call BrownBoost (the reasonforthenamewillbecomeclearshortly).WebelievethatBrownBoostwillbeauseful
网页Speci cally, it is known that the Boost-by-majority (Freund, 1995) algorithm is optimal in a certain sense, and that AdaBoost (Freund and Schapire, 1997) is a practical approximation.
An adaptive version of the boost by majority algorithm
网页TL;DR: A new boosting algorithm is proposed that is an adaptive version of the boost by majority algorithm and combines bounded goals of the boosted algorithm with the adaptivity of AdaBoost.
An Adaptive Version of the Boost By Majority Algorithm
网页2002年12月4日 — The method used for making boost-by-majority adaptive is to consider the limit in which each of the boosting iterations makes an infinitesimally small contribution to the process as a whole. This...
An Adaptive Version of the Boost by Majority Algorithm
网页This paper proposes a natural multiclass extension to the basic algorithm, incorporating error-correcting output codes and a multiclass gain measure, and test two-class and multiclass versions of the algorithm on a number of real and simulated data sets with artificial class noise, showing that Brownboost consistently outperforms Adaboost in the...
The Boosting Approach to Machine Learning: An Overview
网页Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost’s training error and generalization...
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