Novel Maximum-Margin Training Algorithms for Supervised Neural Networks

This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the...

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Veröffentlicht in:	IEEE transactions on neural networks Jg. 21; H. 6; S. 972 - 984
Hauptverfasser:	Ludwig, Oswaldo, Nunes, Urbano
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.06.2010 Institute of Electrical and Electronics Engineers
Schlagworte:	Algorithms Applied sciences Artificial intelligence Back propagation Backpropagation algorithms Complexity Computer science; control theory; systems Computer Simulation Connectionism. Neural networks Constraint optimization Data processing. List processing. Character string processing Exact sciences and technology Feedback Humans Hyperplanes Information Theory Interference Kernel Learning - physiology Mathematical models maximal-margin (MM) principle Memory organisation. Data processing multilayer perceptron (MLP) Multilayer perceptrons Neural networks Neural Networks (Computer) Optimization methods pattern recognition Pattern Recognition, Automated - methods ROC Curve Software supervised learning Support vector machines Testing Training Adaptive algorithm multilayer perceptron (MLP) Gradient descent Backpropagation algorithm Space complexity Vector support machine Learning algorithm maximal-margin (MM) principle Mathematical programming Backpropagation Discriminant analysis Statistical analysis Minimization Pattern recognition Neural network Computational complexity Hyperplane Fisher information Constrained optimization Supervised learning Receiver operating characteristic curves Multilayer perceptrons Objective function Time complexity Artificial intelligence Information theory
ISSN:	1045-9227, 1941-0093, 1941-0093
Online-Zugang:	Volltext
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