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Fast training of recurrent networks based on the EM algorithm

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2 Author(s)
Sheng Ma ; Dept. of Electr. Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA ; Chuanyi Ji

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:

IEEE Transactions on Neural Networks  (Volume:9 ,  Issue: 1 )