0404821 - UIVT-O 20030183 RIV AT eng C - Konferenční příspěvek (zahraniční konf.)
Kůrková, Věra - Sanguineti, M.
Neural Network Learning as Approximate Optimization.
Artificial Neural Nets and Genetic Algorithms. Wien: SpringerVerlag, 2003 - (Pearson, D.; Steele, N.; Albrecht, R.), s. 53-57. ISBN 3-211-00743-1.
[ICANNGA'2003 /6./. Roanne (FR), 23.04.2003-25.04.2003]
Grant CEP: GA ČR GA201/02/0428
Grant ostatní: IT-CZ Area MC6(XX) Project 22
Výzkumný záměr: AV0Z1030915
Klíčová slova: neural networks * learning from data * approximate optimization
Kód oboru RIV: BA - Obecná matematika
DOI: https://doi.org/10.1007/978-3-7091-0646-4_11

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.
Trvalý link: http://hdl.handle.net/11104/0125054