1996 ExperimentswithaNewBoostingAlgo

(Freund & Schapire, 1996) ⇒ Yoav Freund, and Robert E. Schapire. (1996). “Experiments with a New Boosting Algorithm.” In: Proceedings of the 13th International Conference on Machine Learning (ICML-1996).

Subject Headings: AdaBoost Algorithm.

Notes

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 generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a “pseudo-loss” which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems.

We performed two sets of experiments. The first set compared boosting to [[Breiman’s [1] “bagging” method]] when used to aggregate various classifiers (including decision trees and single attribute-value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem..

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1996 ExperimentswithaNewBoostingAlgo	Yoav Freund Robert E. Schapire			Experiments with a New Boosting Algorithm						1996