Automatically assigned DDC number: 00632
Manually assigned DDC number: 00632
Number of references: 4
Title: Regularization With a Pruning Prior
Author:
Author:
Subject: Cyril Goutte,Lars Kai Hansen Regularization With a Pruning Prior
Description: We investigate the use of a regularization prior that we show has pruning properties. Analyses are conducted both using a Bayesian framework and with the generalization method, on a simple toy problem. Results are thoroughly compared with those obtained with a traditional weight decay. Keywords: Regularization, Generalization method, Bayesian learning, Evidence framework, Laplace prior, Comparison with weight decay. 1 Gauss and Laplace priors Regularization by weight decay, and the associated Bayesian interpretation involving a Gaussian prior is a well established part of modern neural computation, see e.g., [KH91]. In [HR94] it was shown that if the weight decay parameter is determined by data, either using MacKay's Evidence procedure, or by minimizing generalization error, pruning may result. In a recent communication, [Wil95] elucidated the interesting properties of the Laplacian prior, in particular he showed that this prior directly leads to pruning of small weights, for any fixe...
Contributor: The Pennsylvania State University CiteSeer Archives
Publisher: unknown
Date: 1996-02-14
Pubyear: 1997
Format: ps
Identifier: http://citeseer.ist.psu.edu/150143.html
Source: http://www-poleia.lip6.fr/CONNEX/Articles/goutte-regularization.ps.gz
Language: en
Relation:
Relation:
Relation:
Relation:
Rights: unrestricted
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