Optimization of neural classifiers based on bayesian decision boundaries and idle neurons pruning
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Data
2002-12-01
Autores
Silvestre, Miriam Rodrigues [UNESP]
Ling, Lee Luan
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Resumo
In this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that realy contribute to correct classification. Also in this article we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method. © 2002 IEEE.
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Bayesian decision boundaries, Neurons, Pruning techniques, Algorithms, Decision theory, Mathematical models, Neural networks, Pattern recognition
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Proceedings - International Conference on Pattern Recognition, v. 16, n. 3, p. 387-390, 2002.