Referenced Items are not available for this document.
In this work, a probabilistic model is established for recurrent networks. The expectation-maximization (EM) algorithm is then applied to derive a new fast training algorithm for recurrent networks through mean-field approximation. This new algorithm converts training a complicated recurrent network into training an array of individual feedforward neurons. These neurons are then trained via a linear weighted regression algorithm. The training time has been improved by five to 15 times on benchmark problems
Published in:
Neural Networks, IEEE Transactions on
(Volume:9
,
Issue:
1
)
Date of Publication: Jan 1998
- Page(s):
- 11 - 26
- ISSN :
- 1045-9227
- INSPEC Accession Number:
- 5813832
- Digital Object Identifier :
- 10.1109/72.655025
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Jan 1998
- Sponsored by :
- IEEE Computational Intelligence Society
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