EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines

被引：0

作者：

Claesen, Marc ^{[1
]}

De Smet, Frank ^{[2
]}

Suykens, Johan A. K. ^{[1
]}

De Moor, Bart ^{[1
]}

机构：

[1] Katholieke Univ Leuven, ESAT STADIUS iMinds Future Hlth, B-3001 Louvain, Belgium

[2] Katholieke Univ Leuven, Dept Publ Hlth & Primary Care, B-3001 Louvain, Belgium

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2014年 / 15卷

关键词：

Classification; ensemble learning; support vector machine; bagging;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models. It currently offers ensemble methods based on binary SVM models. Our implementation avoids duplicate storage and evaluation of support vectors which are shared between constituent models. Experimental results show that using ensemble approaches can drastically reduce training complexity while maintaining high predictive accuracy. The EnsembleSVM software package is freely available online at http://esat.kuleuven.be/stadius/ensemblesvm.

引用

页码：141 / 145

页数：5

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