✦ LIBER ✦
Neural classifier construction using regularization, pruning and test error estimation
✍ Scribed by Mads Hintz-Madsen; Lars Kai Hansen; Jan Larsen; Morten With Pedersen; Michael Larsen
- Book ID
- 104348906
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 375 KB
- Volume
- 11
- Category
- Article
- ISSN
- 0893-6080
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✦ Synopsis
In this paper we propose a method for construction of feed-forward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme, we derive a modified form of the entropic error measure and an algebraic estimate of the test error. In conjunction with optimal brain damage pruning, a test error estimate is used to select the network architecture. The scheme is evaluated on four classification problems.