DocumentCode
1327710
Title
Iterative generation of higher-order nets in polynomial time using linear programming
Author
Roy, Asim ; Mukhopadhyay, Somnath
Author_Institution
Dept. of Decision & Inf. Syst., Arizona State Univ., Tempe, AZ, USA
Volume
8
Issue
2
fYear
1997
fDate
3/1/1997 12:00:00 AM
Firstpage
402
Lastpage
412
Abstract
This paper presents an algorithm for constructing and training a class of higher-order perceptrons for classification problems. The method uses linear programming models to construct and train the net. Its polynomial time complexity is proven and computational results are provided for several well-known problems. In all cases, very small nets were created compared to those reported in other computational studies
Keywords
computational complexity; feedforward neural nets; iterative methods; learning (artificial intelligence); linear programming; multilayer perceptrons; pattern classification; feedforward neural nets; higher-order neural nets; learning; linear programming; multilayer perceptrons; pattern classification; polynomial time complexity; Biological neural networks; Feedforward neural networks; Iterative algorithms; Learning systems; Linear programming; Mathematics; Multi-layer neural network; Multilayer perceptrons; Neural networks; Polynomials;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.557694
Filename
557694
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