Počet záznamů: 1

Genetic Algorithm with Species for Regularization Network Metalearning

  1. 1.
    0348394 - UIVT-O 2011 RIV SK eng C - Konferenční příspěvek (zahraniční konf.)
    Vidnerová, Petra - Neruda, Roman
    Genetic Algorithm with Species for Regularization Network Metalearning.
    Informačné Technológie - Aplikácie a Teória. Seňa: Pont, 2010 - (Pardubská, D.), s. 111-116. ISBN 978-80-970179-3-4.
    [ITAT 2010. Conference on Theory and Practice of Information Technologies. Smrekovica (SK), 21.09.2010-25.09.2010]
    Grant CEP: GA AV ČR KJB100300804
    Výzkumný záměr: CEZ:AV0Z10300504
    Klíčová slova: regularization networks * kernel functions * genetic algorithms
    Kód oboru RIV: IN - Informatika

    Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, although the presence of metaparameters is their drawback. The metaparameters are typically supposed to be given in advance and come ready as an input of the algorithm. Typically, they are set based on the task context by an experienced user. In this paper, we develop a method for finding optimal values of metaparameters, namely the type of kernel function, kernel parameters and regularization parameter. The method is based on co-evolutionary genetic algorithms with different species for different kind
    Trvalý link: http://hdl.handle.net/11104/0188941
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