| Boosting和Bagging综述 |
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| 引用本文： | 沈学华,周志华,吴建鑫,陈兆乾.Boosting和Bagging综述[J].计算机工程与应用,2000,36(12):31-32,40. |
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| 作者姓名： | 沈学华 周志华 吴建鑫 陈兆乾 |
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| 作者单位： | 南京大学计算机软件新技术国家重点实验室,南京,210093 |
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| 摘 要： | Boosting 和 Bagging 是两种用来提高学习算法准确度的方法,这两种方法通过构造一个预测函数系列,然后以一定的方式将它们组合成一个预测函数.文章将介绍这两种方法以及对他们进行的一些理论分析和实验,并对它们的应用以及将来可能的研究进行讨论.
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| 关 键 词： | 机器学习 泛化误差 分类 回归 |
Survey of Boosting and Bagging |
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| Shen Xuehua,Zhou Zhihua,Wu Jianxin,Chen Zhaoqian.Survey of Boosting and Bagging[J].Computer Engineering and Applications,2000,36(12):31-32,40. |
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| Authors: | Shen Xuehua Zhou Zhihua Wu Jianxin Chen Zhaoqian |
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| Abstract: | Boosting and bagging(bootstrap aggregating) are two methods for improving the accuracy of any given learning algorithm,both of which generate multiple versions of a hypothesis and combine them to create an aggregate hypothesis.This brief survey introduces the two methods,describes some theoretical analyses and empirical evidences of their performance,and presents some applications and future research issues. |
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| Keywords: | machine learning generalization error classification regress? |
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