CiteWeb id: 19960000043

CiteWeb score: 7599

In an earlier paper, we introduced a new "boosting" algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that con- sistently 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 ofmulti-label conceptstoconcentrateonthelabelsthatarehardest to discriminate. In this paper,we describeexperiments wecarried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. Weperformedtwosetsofexperiments. Thefirstsetcompared boosting to Breiman's "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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