An Input Output HMM Architecture

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Yoshua Bengio, Paolo Frasconi


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.