[PDF][PDF] Experiments with a new boosting algorithm
Y Freund, RE Schapire - icml, 1996 - Citeseer
Y Freund, RE Schapire
icml, 1996•CiteseerAbstract 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 …
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 …
Abstract
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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