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Learning Errors by Radial Basis Function Neural Networks and Regularization Networks
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SYSNO ASEP 0331128 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Ostatní články Title Learning Errors by Radial Basis Function Neural Networks and Regularization Networks Title Chyby učení u RBF sítí a regularizačních sítí Author(s) Neruda, Roman (UIVT-O) SAI, RID, ORCID
Vidnerová, Petra (UIVT-O) RID, SAI, ORCIDSource Title International Journal of Grid and Distributed Computing - ISSN 2005-4262
Roč. 1, č. 2 (2009), s. 49-57Number of pages 9 s. Language eng - English Country KR - Korea, Republic of Keywords neural network ; RBF networks ; regularization ; learning Subject RIV IN - Informatics, Computer Science R&D Projects 1M0567 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10300504 - UIVT-O (2005-2011) Annotation Regularization theory presents a sound framework to solving supervised learning problems. However, there is a gap between the theoretical results and practical suitability of regularization networks (RN). Radial basis function networks (RBF) that can be seen as a special case of regularization networks have a rich selection of learning algorithms. In this work we study a relationship between RN and RBF, and show that theoretical estimates for RN hold for a concrete RBF applied to real-world data, to a certain degree. This can provide several recommendations for strategies on choosing number of units in RBF network. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2010
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