Parameter mining using the out-of-bootstrap generalisation error estimate for Stochastic Discrimination and Random Forests

被引：0

作者：

Prior, M. ^{[1
]}

Windeatt, T. ^{[1
]}

机构：

[1] Univ Surrey, CVSSP, Guildford GU2 7XH, Surrey, England

来源：

18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS | 2006年

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：

Stochastic Discrimination is a machine learning algorithm with strong theoretical underpinnings and good published results on UCI datasets. However, it has not been popular amongst practitioners. We look at some of the issues involved in its use, propose the Out-of-Bootstrap error estimator as a means of tuning Stochastic Discrimination's and other classifiers' performance and contrast Stochastic Discrimination's utility with that of a related classification technique of Random Forests.

引用

页码：498 / +

页数：2