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Adaptive conjugate gradient algorithm for perceptron training

DSpace at IIT Bombay

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Field Value
 
Title Adaptive conjugate gradient algorithm for perceptron training
 
Creator NAGARAJA, G
BOSE, RPJC
 
Subject linear inequalities
perceptron
conjugate-gradient
linear separability
linear inequalities
training
 
Description An adaptive algorithm for function minimization based on conjugate gradients for the problem of finding linear discriminant functions in pattern classification is developed. The algorithm converges to a solution in both consistent and inconsistent cases in a finite number of steps on several datasets. We have applied our algorithm and compared its performance with the adaptive versions of the Ho-Kashyap procedure (AHK). We have also compared the batch version of the algorithm with the batch mode AHK. The results show that the proposed adaptive conjugate gradient algorithm (CGA) gives vastly superior performance in terms of both the number of training cycles required and the classification rate. Also, the batch mode CGA performs much better than the batch mode AHK. (c) 2005
 
Publisher ELSEVIER SCIENCE BV
 
Date 2011-07-24T08:00:25Z
2011-12-26T12:55:38Z
2011-12-27T05:41:44Z
2011-07-24T08:00:25Z
2011-12-26T12:55:38Z
2011-12-27T05:41:44Z
2006
 
Type Article
 
Identifier NEUROCOMPUTING, 69(4-6), 368-386
0925-2312
http://dx.doi.org/10.1016/j.neucom.2005.03.007
http://dspace.library.iitb.ac.in/xmlui/handle/10054/6362
http://hdl.handle.net/10054/6362
 
Language en