Original title: Model Complexity in Learning from High-Dimensional Data
Translated title: Modelová složitost při učení na základě vysoce dimensionálních dat
Authors: Kůrková, Věra
Document type: Papers
Conference/Event: SOFSEM 2009. Conference on Current Trends in Theory and Practice of Computer Science /35./, Špindlerův Mlýn (CZ), 2009-01-24 / 2009-01-30
Year: 2008
Language: eng
Abstract: [eng] [cze]
Model complexity in learning from high dimensional data is estimated using methods of approximation and integration theory. Odhady modelové složitosti při učení na základě vysoce dimenzionálních dat jsou odvozeny pomocí metod teorie aproximace a integrace.
Keywords: complexity of neural networks; high-dimensional data; learning
Project no.: CEZ:AV0Z10300504 (CEP), 1ET100300517 (CEP)
Funding provider: GA AV ČR
Host item entry: SOFSEM 2009: Theory and Practice of Computer Science, ISBN 978-80-7378-059-3

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at the institute of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0170439

Permalink: http://www.nusl.cz/ntk/nusl-39976


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Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers