Experiments with a New Boosting Algorithm

机译：使用新的Boosting算法进行实验

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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 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.rnWe performed two sets of experiments. The first set compared 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.

机译：在较早的论文中，我们介绍了一种称为AdaBoost的新“增强”算法，从理论上讲，该算法可用于显着减少任何能够持续生成分类器的学习算法，该分类器的性能要比随机猜测好一点。我们还介绍了“伪损失”的相关概念，它是一种强制多标签概念的学习算法专注于最难以区分的标签的方法。在本文中，我们描述了为评估AdaBoost在有无伪损失的情况下在实际学习问题上的表现而进行的实验。我们进行了两组实验。第一组将提升与Breiman的“装袋”方法进行了比较，该方法用于汇总各种分类器（包括决策树和单个属性值测试）。我们在一组机器学习基准上比较了这两种方法的性能。在第二组实验中，我们更详细地研究了在OCR问题上使用最近邻分类器进行增强的性能。

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《Machine learning》|1996年|148-156|共9页
会议地点 Bari(IT);Bari(IT)
作者
Yoav Freund; Robert E. Schapire;
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ATT Laboratories 600 Mountain Avenue Murray Hill, NJ 07974-0636;

ATT Laboratories 600 Mountain Avenue Murray Hill, NJ 07974-0636;

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原文格式 PDF
正文语种 eng
中图分类计算机的应用;
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3. Discussion of “the evolution of boosting algorithms” and “extending statistical boosting” [J] . BühlmannP., GertheissJ., HiekeS., Methods of information in medicine . 2014,第6期

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4. Research on Applied Technology in Experiments with three Boosting Algorithms [C] . Xiaowei Sun, Hongbo Zhou International Conference on Environmental Science and Material Application . 2014

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5. Hypothesis margin based weighting for feature selection using boosting: Theory, algorithms and applications. [D] . Alshawabkeh, Malak. 2013

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6. Comparison of the Predicting Performance for Fate of Medial Meniscus Posterior Root Tear Based on Treatment Strategies: A Comparison between Logistic Regression Gradient Boosting and CNN Algorithms [O] . Jae-Ik Lee, Dong-Hyun Kim, Hyun-Jin Yoo, 2021

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7. Experiments with Two New Boosting Algorithms [O] . Hongbo Zhou, Xiaowei Sun 2010

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Experiments with a New Boosting Algorithm

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