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Neural Network Learning as Approximate Optimization
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SYSNO ASEP 0404821 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Neural Network Learning as Approximate Optimization Author(s) Kůrková, Věra (UIVT-O) RID, SAI, ORCID
Sanguineti, M. (IT)Source Title Artificial Neural Nets and Genetic Algorithms / Pearson D. W. ; Steele N. C. ; Albrecht R. F.. - Wien : SpringerVerlag, 2003 - ISBN 3-211-00743-1 Pages s. 53-57 Number of pages 5 s. Action ICANNGA'2003 /6./ Event date 23.04.2003-25.04.2003 VEvent location Roanne Country FR - France Event type WRD Language eng - English Country AT - Austria Keywords neural networks ; learning from data ; approximate optimization Subject RIV BA - General Mathematics R&D Projects GA201/02/0428 GA ČR - Czech Science Foundation (CSF) CEZ 1030915 UT WOS 000183288200011 DOI 10.1007/978-3-7091-0646-4_11 Annotation Learning from data will be studied in the framework of approximate minimization of regularized empirical error functionals. There will be derived estimates of speed of convergence of infima achievable over approximations of an admissible set to a global infimum. The results will be applied to empirical error functionals regularized using stabilizers defined as squares of norms in reproducing kernel Hilbert spaces. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2004
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