Počet záznamů: 1

Hybrid Learning of Regularization Neural Networks

  1. 1.
    0345012 - UIVT-O 2011 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
    Vidnerová, Petra - Neruda, Roman
    Hybrid Learning of Regularization Neural Networks.
    Artificial Intelligence and Soft Computing. Vol. 2. Berlin: Springer, 2010 - (Rutkowski, L.; Scherer, R.; Tadeusiewicz, R.; Zadeh, L.; Zurada, J.), s. 124-131. Lecture Notes in Artificial Intelligence, 6114. ISBN 978-3-642-13231-5. ISSN 0302-9743.
    [ICAISC 2010. International Conference on Artifical Intelligence and Soft Computing /10./. Zakopane (PL), 13.06.2010-17.06.2010]
    Grant CEP: GA AV ČR KJB100300804
    Výzkumný záměr: CEZ:AV0Z10300504
    Klíčová slova: supervised learning * regularization networks * genetic algorithms
    Kód oboru RIV: IN - Informatika

    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.
    Trvalý link: http://hdl.handle.net/11104/0186392
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