EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
Abstract
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 esat.kuleuven.be/stadius/ensemblesvm.
Cite
Text
Claesen et al. "EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines." Machine Learning Open Source Software, 2014.Markdown
[Claesen et al. "EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines." Machine Learning Open Source Software, 2014.](https://mlanthology.org/mloss/2014/claesen2014jmlr-ensemblesvm/)BibTeX
@article{claesen2014jmlr-ensemblesvm,
title = {{EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines}},
author = {Claesen, Marc and De Smet, Frank and Suykens, Johan A.K. and De Moor, Bart},
journal = {Machine Learning Open Source Software},
year = {2014},
pages = {141-145},
volume = {15},
url = {https://mlanthology.org/mloss/2014/claesen2014jmlr-ensemblesvm/}
}