Document Detail


General Gaussian Priors for Improved Generalization.
MedLine Citation:
PMID:  12662573     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
We explore the dependence of performance measures, such as the generalization error and generalization consistency, on the structure and the parametrization of the prior on "rules", instanced here by the noisy linear perceptron. Using a statistical mechanics framework, we show how one may assign values to the parameters of a model for a "rule" on the basis of data instancing the rule. Information about the data, such as input distribution, noise distribution and other "rule" characteristics may be embedded in the form of general Gaussian priors for improving net performance. We examine explicitly two types of general Gaussian priors which are useful in some simple cases. We calculate the optimal values for the parameters of these priors and show their effect in modifying the most probable, MAP, values for the rules. Copyright 1996 Elsevier Science Ltd.
Authors:
D Saad
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Neural networks : the official journal of the International Neural Network Society     Volume:  9     ISSN:  1879-2782     ISO Abbreviation:  Neural Netw     Publication Date:  1996 Aug 
Date Detail:
Created Date:  2003-Mar-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8805018     Medline TA:  Neural Netw     Country:  -    
Other Details:
Languages:  ENG     Pagination:  937-945     Citation Subset:  -    
Affiliation:
The University of Edinburgh, UK
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