Skip Navigation

Logic Journal of IGPL 2005 13(5):551-559; doi:10.1093/jigpal/jzi041
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Kurková, V.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author, 2005. Published by Oxford University Press. All rights reserved.

Original Articles

Neural Network Learning as an Inverse Problem

Vera Kurková

Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou vezí 2, 182 07 Prague 8, Czech Republic. E-mail: vera{at}cs.cas.cz

Capability of generalization in learning of neural networks from examples can be modelled using regularization, which has been developed as a tool for improving stability of solutions of inverse problems. Such problems are typically described by integral operators. It is shown that learning from examples can be reformulated as an inverse problem defined by an evaluation operator. This reformulation leads to an analytical description of an optimal input/output function of a network with kernel units, which can be employed to design a learning algorithm based on a numerical solution of a system of linear equations.

Key Words: learning from data, generalization, empirical error functional, inverse problem, evaluation operator, kernel methods

Received 5 January 2005.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.