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- Yoav Freund, Robert E. Schapire
- J. Comput. Syst. Sci.
- 1997

In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a… (More)

- Yoav Freund, Robert E. Schapire
- ICML
- 1996

In an earlier paper [9], we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently… (More)

- Robert E. Schapire, Yoram Singer
- COLT
- 1998

We describe several improvements to Freund and Schapire’s AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a… (More)

- Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer
- ICML
- 1998

The problem of combining preferences arises in several applications, such as combining the results of di erent search engines. This work describes an eÆcient algorithm for combining multiple… (More)

- Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire
- SIAM J. Comput.
- 2002

In the multiarmed bandit problem, a gambler must decide which arm of K nonidentical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received… (More)

Boosting is a general method for improving the accuracy of any given learning algorithm. This short overview paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of… (More)

- Robert E. Schapire, Yoram Singer
- Machine Learning
- 2000

This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We… (More)

- Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee
- ICML
- 1997

One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated hypothesis usually does not increase as its size becomes very large, and often… (More)

- Robert E. Schapire
- Machine Learning
- 1990

- Erin L. Allwein, Robert E. Schapire, Yoram Singer
- ICML
- 2000

We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learning… (More)