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Learning from Data as an Optimization and Inverse Problem
- 1.0376629 - ÚI 2013 RIV DE eng C - Conference Paper (international conference)
Kůrková, Věra
Learning from Data as an Optimization and Inverse Problem.
Computational Intelligence. Heidelberg: Springer, 2012 - (Madani, K.; Correia, A.; Rosa, A.; Filipe, J.), s. 361-372. Studies in Computational Intelligence, 399. ISBN 978-3-642-27533-3. ISSN 1860-949X.
[IJCCI 2010. International Joint Conference on Computational Intelligence. Valencia (ES), 24.10.2010-26.10.2010]
R&D Projects: GA ČR GAP202/11/1368
Institutional research plan: CEZ:AV0Z10300504
Keywords : learning from data * minimization of empirical error * inverse problems * reproducing kernel Hilbert spaces
Subject RIV: IN - Informatics, Computer Science
Learning form data is investigated as minimization of empirical error functional in spaces of continuous functions and spaces defined by kernels. Using methods from theory of inverse problems, an alternative proof of Representer Theorem is given. Regularized and non regularized minimization of empirical error is compared.
Permanent Link: http://hdl.handle.net/11104/0007313
Number of the records: 1