EnsembleSVM is a library providing an API to implement ensemble learning use Support Vector Machine (SVM) base models. The package contains some executable tools which behave similar to standard SVM learning algorithms.
The package is self-contained in the sense that it contains most necessary tools to build a pipeline for binary classification. Most notable features include bootstrap sampling, cross-validation and ensemble training/prediction.
The EnsembleSVM webpage contains all sorts of useful information at: http://esat.kuleuven.be/stadius/ensemblesvm/
EnsembleSVM uses a divide-and-conquer strategy to handle large data sets by training base models on (small) subsamples and aggregating these base models into a strong ensemble.
If you use EnsembleSVM, please cite our paper:
M. Claesen, F. De Smet, J. Suykens, and B. De Moor, "EnsembleSVM: A library for ensemble learning using support vector machines", Journal of Machine Learning Research, vol. 15, pp. 141–145, 2014.
For installation instructions, please refer to the file INSTALL or our webpage. Our webpage also contains an elaborate user manual and some use cases to familiarise users with the software.
Please report bugs to firstname.lastname@example.org.
EnsembleSVM is released under the General Lesser Public License version 3 (GPLv3+). See the file COPYING.LESSER for the license agreement.
Copyright (C) 2013-2014 KU Leuven
Copying and distribution of this file, with or without modification, are permitted in any medium without royalty provided the copyright notice and this notice are preserved. This file is offered as-is, without warranty of any kind.