Faster RBF Network Learning Utilizing Singular Regions

Seiya Satoh, Ryohei Nakano

2019

Abstract

There are two ways to learn radial basis function (RBF) networks: one-stage and two-stage learnings. Recently a very powerful one-stage learning method called RBF-SSF has been proposed, which can stably find a series of excellent solutions, making good use of singular regions, and can monotonically decrease training error along with the increase of hidden units. RBF-SSF was built by applying the SSF (singularity stairs following) paradigm to RBF networks; the SSF paradigm was originally and successfully proposed for multilayer perceptrons. Although RBF-SSF has the strong capability to find excellent solutions, it required a lot of time mainly because it computes the Hessian. This paper proposes a faster version of RBF-SSF called RBF-SSF(pH) by introducing partial calculation of the Hessian. The experiments using two datasets showed RBF-SSF(pH) ran as fast as usual one-stage learning methods while keeping the excellent solution quality.

Paper Citation

in Bibtex Style

@conference{icpram19,
author={Seiya Satoh and Ryohei Nakano},
title={Faster RBF Network Learning Utilizing Singular Regions},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={501-508},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007367205010508},
isbn={978-989-758-351-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Faster RBF Network Learning Utilizing Singular Regions
SN - 978-989-758-351-3
AU - Satoh S.
AU - Nakano R.
PY - 2019
SP - 501
EP - 508
DO - 10.5220/0007367205010508

in Harvard Style

Satoh S. and Nakano R. (2019). Faster RBF Network Learning Utilizing Singular Regions.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 501-508. DOI: 10.5220/0007367205010508