An Input Output HMM Architecture

An Input Output HMM Architecture (1995)

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by Yoshua Bengio , Paolo Frasconi

Venue:	ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
Citations:	97 - 14 self

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@INPROCEEDINGS{Bengio95aninput,
    author = {Yoshua Bengio and Paolo Frasconi},
    title = {An Input Output HMM Architecture},
    booktitle = {ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS },
    year = {1995},
    pages = {427--434},
    publisher = {MIT Press}
}

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Abstract

We introduce a recurrent architecture having a modular structure and we formulate a training procedure based on the EM algorithm. The resulting model has similarities to hidden Markov models, but supports recurrent networks processing style and allows to exploit the supervised learning paradigm while using maximum likelihood estimation.

Citations

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63	Global optimization of a neural network—Hidden Markov model hybrid - Bengio, Mori, et al. - 1991
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36	Dynamic construction of finite-state automata from examples using hill-climbing - Tomita - 1982
25	Credit assignment through time: Alternatives to backpropagation - Bengio, Frasconi - 1994
11	An EM approach to learning sequential behavior - Bengio, Frasconi - 1994
10	Links between Hidden Markov Models and Multilayer - Bourlard, Wellekens - 1990