Silvestre, Miriam Rodrigues [UNESP]Ling, Lee Luan2014-05-272014-05-272002-12-01Proceedings - International Conference on Pattern Recognition, v. 16, n. 3, p. 387-390, 2002.1051-4651http://hdl.handle.net/11449/67053In 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.387-390engBayesian decision boundariesNeuronsPruning techniquesAlgorithmsDecision theoryMathematical modelsNeural networksPattern recognitionTrabalho apresentado em evento10.1109/ICPR.2002.1047927WOS:000177887100094Acesso aberto2-s2.0-337515753033356686459975471