Number of the records: 1
Hybrid Learning of Regularization Neural Networks
- 1.
SYSNO ASEP 0345012 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Hybrid Learning of Regularization Neural Networks Author(s) Vidnerová, Petra (UIVT-O) RID, SAI, ORCID
Neruda, Roman (UIVT-O) SAI, RID, ORCIDSource Title Artificial Intelligence and Soft Computing, 2. - Berlin : Springer, 2010 / Rutkowski L. ; Scherer R. ; Tadeusiewicz R. ; Zadeh L.A. ; Zurada J.M. - ISSN 0302-9743 - ISBN 978-3-642-13231-5 Pages s. 124-131 Number of pages 8 s. Action ICAISC 2010. International Conference on Artifical Intelligence and Soft Computing /10./ Event date 13.06.2010-17.06.2010 VEvent location Zakopane Country PL - Poland Event type WRD Language eng - English Country DE - Germany Keywords supervised learning ; regularization networks ; genetic algorithms Subject RIV IN - Informatics, Computer Science R&D Projects KJB100300804 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000281548200015 EID SCOPUS 77955445838 DOI 10.1007/978-3-642-13232-2_15 Annotation Regularization theory presents a sound framework to solving supervised learning problems. However, the regularization networks have a large size corresponding to the size of training data. In this work we study a relationship between network complexity, i.e. number of hidden units, and approximation and generalization ability. We propose an incremental hybrid learning algorithm that produces smaller networks with performance similar to original regularization networks. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2011
Number of the records: 1