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A mean field theory learning algorithm for neural networks

Peterson, Carsten LU and Anderson, James R (1987) In Complex Systems 1. p.995-1019
Abstract
Based on t he Boltzmann Machine concept, we derive a
lear ning algorithm in which time-consuming stochastic measurements
of correlations a re replaced by solutions to dete rminist ic mean field
theory equ ations. T he method is applied to t he XOR (exclusive-or ),
encoder, and line sym metry problems with substantial success. We
observe speedup facto rs ranging from 10 to 30 for these ap plicat ions
and a significan tly bet ter learning performan ce in general.
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Complex Systems
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1
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24 pages
language
English
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624aca74-da8b-41ae-a50b-906635925902
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@article{624aca74-da8b-41ae-a50b-906635925902,
  abstract     = {{Based on t he Boltzmann Machine concept, we derive a<br/>lear ning algorithm in which time-consuming stochastic measurements<br/>of correlations a re replaced by solutions to dete rminist ic mean field<br/>theory equ ations. T he method is applied to t he XOR (exclusive-or ),<br/>encoder, and line sym metry problems with substantial success. We<br/>observe speedup facto rs ranging from 10 to 30 for these ap plicat ions<br/>and a significan tly bet ter learning performan ce in general.}},
  author       = {{Peterson, Carsten and Anderson, James R}},
  language     = {{eng}},
  pages        = {{995--1019}},
  series       = {{Complex Systems}},
  title        = {{A mean field theory learning algorithm for neural networks}},
  volume       = {{1}},
  year         = {{1987}},
}