Title :
A unified approach on fast training of feedforward and recurrent networks using EM algorithm
Author :
Ma, Sheng ; Ji, Chuanyi
Author_Institution :
Dept. of Electr. Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA
fDate :
8/1/1998 12:00:00 AM
Abstract :
In this work, we provide a theoretical framework that unifies the notions of hidden representations and moving targets through the expectation and maximization (EM) algorithm. Based on such a framework, two fast training algorithms can be derived consistently for both feedforward networks and recurrent networks
Keywords :
feedforward neural nets; learning (artificial intelligence); random processes; recurrent neural nets; signal representation; EM algorithm; expectation and maximization algorithm; fast training algorithms; feedforward networks; hidden representations; moving targets; recurrent networks; unified approach; Heuristic algorithms; Joining processes; Maximum likelihood estimation; Multi-layer neural network; Nonhomogeneous media; Recurrent neural networks; Signal processing; Signal processing algorithms; Speech processing; Statistics;
Journal_Title :
Signal Processing, IEEE Transactions on
