Boosting methods for regression

被引：123

作者：

Duffy, N ^{[1
]}

Helmbold, D ^{[1
]}

机构：

[1] Univ Calif Santa Cruz, Dept Comp Sci, Santa Cruz, CA 95064 USA

来源：

MACHINE LEARNING | 2002年 / 47卷 / 2-3期

基金：

美国国家科学基金会;

关键词：

learning; boosting; arcing; ensemble methods; regression; gradient descent;

D O I：

10.1023/A:1013685603443

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we examine ensemble methods for regression that leverage or "boost" base regressors by iteratively calling them on modified samples. The most successful leveraging algorithm for classification is AdaBoost, an algorithm that requires only modest assumptions on the base learning method for its strong theoretical guarantees. We present several gradient descent leveraging algorithms for regression and prove AdaBoost-style bounds on their sample errors using intuitive assumptions on the base learners. We bound the complexity of the regression functions produced in order to derive PAC-style bounds on their generalization errors. Experiments validate our theoretical results.

引用

页码：153 / 200

页数：48