DocumentCode
1092881
Title
A learning law for density estimation
Author
Modha, Dharmendra S. ; Fainman, Yeshayahu
Author_Institution
Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
Volume
5
Issue
3
fYear
1994
fDate
5/1/1994 12:00:00 AM
Firstpage
519
Lastpage
523
Abstract
Probability density functions are estimated by an exponential family of densities based on multilayer feedforward networks. The role of the multilayer feedforward networks, in the proposed estimator, is to approximate the logarithm of the probability density functions. The method of maximum likelihood is used, as the main contribution, to derive an unsupervised backpropagation learning law to estimate the probability density functions. Computer simulation results demonstrating the use of the derived learning law are presented
Keywords
backpropagation; feedforward neural nets; probability; exponential family; maximum likelihood; multilayer feedforward networks; probability density function estimation; unsupervised backpropagation learning law; Feedforward neural networks; Function approximation; Linear approximation; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurons; Optimized production technology; Upper bound;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.286931
Filename
286931
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