Averaged boosting: A noise-robust ensemble method

被引：0

作者：

Kim, Y ^{[1
]}

机构：

[1] Ewha Womans Univ, Dept Stat, Seoul 120750, South Korea

来源：

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING | 2003年 / 2637卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new noise robust ensemble method called "Averaged Boosting (A-Boosting)" is proposed. Using the hypothetical ensemble algorithm in Hilbert space, we explain that A-Boosting can be understood as a method of constructing a sequence of hypotheses and coefficients such that the average of the product of the base hypotheses and coefficients converges to the desirable function. Empirical studies showed that A-Boosting outperforms Bagging for low noise cases and is more robust than AdaBoost to label noise.

引用

页码：388 / 393

页数：6

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