ENSEMBLE RANDOM-SUBSET SVM

Kenji Nishida, Jun Fujiki, Takio Kurita

2011

Abstract

In this paper, the Ensemble Random-Subset SVM algorithm is proposed. In a random-subset SVM, multiple SVMs are used, and each SVM is considered a weak classifier; a subset of training samples is randomly selected for each weak classifier with randomly set parameters, and the SVMs with optimal weights are combined for classification. A linear SVM is adopted to determine the optimal kernel weights; therefore, an ensemble random-subset SVMis based on a hierarchical SVMmodel. An ensemble random-subset SVM outperforms a single SVMeven when using a small number of samples (10 or 100 samples out of 20,000 training samples for each weak classifier); in contrast, a single SVM requires more than 4,000 support vectors.

Paper Citation

in Harvard Style

Nishida K., Fujiki J. and Kurita T. (2011). ENSEMBLE RANDOM-SUBSET SVM . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 334-339. DOI: 10.5220/0003668903340339

in Bibtex Style

@conference{ncta11,
author={Kenji Nishida and Jun Fujiki and Takio Kurita},
title={ENSEMBLE RANDOM-SUBSET SVM},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={334-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003668903340339},
isbn={978-989-8425-84-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - ENSEMBLE RANDOM-SUBSET SVM
SN - 978-989-8425-84-3
AU - Nishida K.
AU - Fujiki J.
AU - Kurita T.
PY - 2011
SP - 334
EP - 339
DO - 10.5220/0003668903340339