| DESIGN AND EVALUATION OF NEURAL CLASSIFIERS Mads Hintz-Madsen, Morten (2007) | |||||||||||||||
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| Abstract- In this paper we propose a method for design 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 the test error estimate is used to optimize the network architecture. The scheme is evaluated on an artificial and a real world problem. | |||||||||||||||
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