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Boosting家族Boost-by-majority系列代表算法 被引量：4

The Typical Algorithm of Boost-by-Majority Series in Boosting Family
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摘要 1 引言 Boosting由Freund和Schapire于1990年提出,是提高预测学习系统预测能力的有效工具,也是组合学习中最具代表性的方法,其代表算法可分为Boost-by-majority和AdaBoost两个系列。Boosting操纵训练例子以产生多个假设。从而建立通过投票结合的预测器集合。Boosting在训练例子上维护一套概率分布。 Boosting is one of the most representational ensemble prediction methods. It can be divided into two series : Boost-by-majority and Adaboost. This paper briefly introduces the research status of Boosting and one of its serials-Boost-by-majority .analyzes the typical algorithms of Boost-by-majority.
作者 涂承胜 刁力力 鲁明羽 陆玉昌
机构地区 重庆三峡学院计算机科学系 清华大学计算机科学技术系
出处 《计算机科学》 CSCD 北大核心 2003年第4期133-135,共3页 Computer Science
关键词 学习算法 BOOSTING算法 Boost-by-majority系列算法 组合学习 Data mining,Machine learning,Combining prediction,Algorithms
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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参考文献13

  • 1刁力力,胡可云,陆玉昌,石纯一.数据挖掘与组合学习[J].计算机科学,2001,28(7):73-78. 被引量：18
  • 2涂承胜,刁力力,鲁明羽,陆玉昌.Boosting家族AdaBoost系列代表算法[J].计算机科学,2003,30(3):30-34. 被引量：27
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  • 7Freund Y. Boosting a weak learning algorithm by majority.Information and computation,1995,121(2) :256~285.
  • 8Freund Y, Schapire R E. A decision-theoretic generalization of online learning and an application to boosting.Journal of Computer and System Science, 1997,55(1) : 119~139.
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二级参考文献21

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共引文献43

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二级引证文献27

计算机科学
2003年 第4期

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