Optimal Control for training: The missing link between Hidden Markov Models and Connectionist Networks

[1]  Hervé Bourlard,et al.  Speech dynamics and recurrent neural networks , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[2]  L. R. Rabiner,et al.  An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition , 1983, The Bell System Technical Journal.

[3]  Yann LeCun,et al.  A theoretical framework for back-propagation , 1988 .

[4]  G. Siouris,et al.  Optimum systems control , 1979, Proceedings of the IEEE.

[5]  Jenq-Neng Hwang,et al.  A Unified Systolic Architecture for Artificial Neural Networks , 1989, J. Parallel Distributed Comput..

[6]  Jenq-Neng Hwang,et al.  A unifying viewpoint of multilayer perceptrons and hidden Markov models , 1989, IEEE International Symposium on Circuits and Systems,.

[7]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[8]  H. Bourlard,et al.  Links Between Markov Models and Multilayer Perceptrons , 1990, IEEE Trans. Pattern Anal. Mach. Intell..