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Authors: Seiya Satoh 1 and Ryohei Nakano 2

Affiliations: 1 Tokyo Denki University, Ishizaka, Hatoyama-machi, Hiki-gun, Saitama 350-0394 and Japan ; 2 Chubu University, 1200 Matsumoto-cho, Kasugai, 487-8501 and Japan

ISBN: 978-989-758-351-3

Keyword(s): Neural Networks, RBF Networks, Learning Method, Singular Region, Reducibility Mapping.

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.

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Paper citation in several formats:
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

@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},
}

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

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