Neighborhood based Levenberg-Marquardt algorithm for neural network training

Although the Levenberg-Marquardt (LM) algorithm has been extensively applied as a neural-network training method, it suffers from being very expensive, both in memory and number of operations required, when the network to be trained has a significant number of adaptive weights. In this paper, the be...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 13; no. 5; pp. 1200 - 1203
Main Authors Lera, G., Pinzolas, M.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.09.2002
Subjects
Adaptive algorithms
Algorithms
Backpropagation algorithms
Computation
Computer languages
Iterative methods
Multilayer perceptrons
Networks
Neural networks
Neurons
Occupation
Optimization methods
Software performance
Software tools
Testing
Training
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Summary:Although the Levenberg-Marquardt (LM) algorithm has been extensively applied as a neural-network training method, it suffers from being very expensive, both in memory and number of operations required, when the network to be trained has a significant number of adaptive weights. In this paper, the behavior of a recently proposed variation of this algorithm is studied. This new method is based on the application of the concept of neural neighborhoods to the LM algorithm. It is shown that, by performing an LM step on a single neighborhood at each training iteration, not only significant savings in memory occupation and computing effort are obtained, but also, the overall performance of the LM method can be increased.
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ISSN:1045-9227
1941-0093
DOI:10.1109/TNN.2002.1031951