CONVEX COMBINATIONS OF MAXIMUM MARGIN BAYESIAN NETWORK CLASSIFIERS

Sebastian Tschiatschek, Franz Pernkopf

2012

Abstract

Maximum margin Bayesian networks (MMBN) can be trained by solving a convex optimization problem using, for example, interior point (IP) methods (Guo et al., 2005). However, for large datasets this training is computationally expensive (in terms of runtime and memory requirements). Therefore, we propose a less resource intensive batch method to approximately learn a MMBN classifier: we train a set of (weak) MMBN classifiers on subsets of the training data, and then exploit the convexity of the original optimization problem to obtain an approximate solution, i.e., we determine a convex combination of the weak classifiers. In experiments on different datasets we obtain similar results as for optimal MMBN determined on all training samples. However, in terms of computational efficiency (runtime) we are faster and the memory requirements are much lower. Further, the proposed method facilitates parallel implementation.

Paper Citation

in Harvard Style

Tschiatschek S. and Pernkopf F. (2012). CONVEX COMBINATIONS OF MAXIMUM MARGIN BAYESIAN NETWORK CLASSIFIERS . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-98-0, pages 69-77. DOI: 10.5220/0003770300690077

in Bibtex Style

@conference{icpram12,
author={Sebastian Tschiatschek and Franz Pernkopf},
title={CONVEX COMBINATIONS OF MAXIMUM MARGIN BAYESIAN NETWORK CLASSIFIERS},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2012},
pages={69-77},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003770300690077},
isbn={978-989-8425-98-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - CONVEX COMBINATIONS OF MAXIMUM MARGIN BAYESIAN NETWORK CLASSIFIERS
SN - 978-989-8425-98-0
AU - Tschiatschek S.
AU - Pernkopf F.
PY - 2012
SP - 69
EP - 77
DO - 10.5220/0003770300690077