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Computational Properties of Probabilistic Neural Networks
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SYSNO ASEP 0350163 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Computational Properties of Probabilistic Neural Networks Author(s) Grim, Jiří (UTIA-B) RID, ORCID
Hora, Jan (UTIA-B)Source Title Artificial Neural Networks – ICANN 2010, Part III. - Berlin Heidelberg : Springer Verlag, 2010 / Diamantaras K. ; Duch Wlodzislaw ; Iliadis L.S. - ISBN 978-3-642-15818-6 Pages s. 31-40 Number of pages 10 s. Action ICANN 2010. International Conference on Artificial Neural Networks /20./ Event date 15.09.2010-18.09.2010 VEvent location Thessaloniki Country GR - Greece Event type WRD Language eng - English Country DE - Germany Keywords Probabilistic neural networks ; Statistical pattern recognition ; Subspace approach ; Overfitting reduction Subject RIV IN - Informatics, Computer Science R&D Projects GA102/07/1594 GA ČR - Czech Science Foundation (CSF) 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10750506 - UTIA-B (2005-2011) UT WOS 000290245400004 DOI 10.1007/978-3-642-15825-4_4 Annotation We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a statistically justified subspace method of classification. The underlying structural mixture model includes binary structural parameters and can be optimized by EM algorithm in full generality. Formally, the structural model reduces the number of parameters included and therefore the structural mixtures become less complex and less prone to overfitting. We illustrate how recognition accuracy and the effect of overfitting is influenced by mixture complexity and by the size of training data set. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2011
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