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An efficient EM-based training algorithm for feedforward neural networks

delete1997-03-01
delete21
PRE
AI
S
Sheng Ma
C
Chuanyi Ji
J
James T. Farmer
DOI：10.1016/S0893-6080(96)00049-4delete
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Abstract

Abstract

En 中文
A fast training algorithm is developed for two-layer feedforward neural networks based on a probabilistic model for hidden representations and the EM algorithm. The algorithm decomposes training the original two-layer networks into training a set of single neurons. The individual neurons are then trained via a linear weighted regression algorithm. Significant improvement on training speed has been made using this algorithm for several bench-mark problems. (C) 1997 Elsevier Science Ltd. All Rights Reserved.
Keywords:
EM algorithm
feedforward networks
fast training
probability model of feedforward networks
hidden representations
linear weighted regression
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Neural Networks cover
Neural Networks
IF:
6.3
Papers:
7.8K
Citations:
3.0W

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