Title :
Training of neural classifiers by separating distributions at the hidden layer
Author :
Góez, Roger ; Lázaro, Marcelino
Author_Institution :
Dept. de Teor. de la Senal y Comun., Univ. Carlos III de Madrid, Leganes, Spain
Abstract :
A new cost function for training of binary classifiers based on neural networks is proposed. This cost function aims at separating the distributions for patterns of each class at the output of the hidden layer of the network. It has been implemented in a Generalized Radial Basis Function (GRBF) network and its performance has been evaluated under three different databases, showing advantages with respect to the conventional Mean Squared Error (MSE) cost function. With respect to the Support Vector Machine (SVM) classifier, the proposed method has also advantages both in terms of performance and complexity.
Keywords :
learning (artificial intelligence); mean square error methods; pattern classification; radial basis function networks; support vector machines; binary classifiers training; generalized radial basis function network; mean squared error cost function; network hidden layer; neural classifiers training; neural networks; support vector machine classifier; Artificial neural networks; Bayesian methods; Cost function; Curve fitting; Databases; Function approximation; Neural networks; Speech recognition; Support vector machine classification; Support vector machines;
Conference_Titel :
Machine Learning for Signal Processing, 2009. MLSP 2009. IEEE International Workshop on
Conference_Location :
Grenoble
Print_ISBN :
978-1-4244-4947-7
Electronic_ISBN :
978-1-4244-4948-4
DOI :
10.1109/MLSP.2009.5306240
