MAXIMUM A POSTERIORI DECISION AND EVALUATION OF CLASS PROBABILITIES BY BOLTZMANN PERCEPTRON CLASSIFIERS

作     者：YAIR, E GERSHO, A 

作者机构：UNIV CALIF SANTA BARBARACTR INFORMAT PROC RESDEPT ELECT & COMP ENGNSANTA BARBARACA 93106 

出 版 物：《PROCEEDINGS OF THE IEEE》 (电气与电子工程师学会会报)

年 卷 期：1990年第78卷第10期

页      面：1620-1628页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Rockwell International Corporation Weizmann Foundation for scientific research University of California, UC 

主　　题：Boltzmann perceptron classifiers decision theory input-output mapping neural classifier neural nets neural network pattern recognition probability probability distribution functions 

摘      要：It is shown that neural network architectures may offer a valuable alternative to the Bayesian classifier. With neural networks, the a posteriori probabilities are computed with no a priori assumptions about the probability distribution functions (PDFs) that generate the data. Rather than assuming certain types of PDFs for the input data, the neural classifier uses a general type of input-output mapping which is then designed to optimally comply with a given set of examples called the training set. It is demonstrated that the a posteriori class probabilities can be efficiently computed by a deterministic feedforward network which is called the Boltzmann perceptron classifier (BPC). Maximum a posteriori (MAP) classifiers are also constructed as a special case of the BPC. Structural relationships between the BPC and a conventional multilayer perceptron (MLP) are given, and it is demonstrated that rather intricate boundaries between classes can be formed even with a relatively modest number of network units. Simulation results show that the BPC is comparable in performance to a Bayesian classifier