منابع مشابه
Input-output HMMs for sequence processing
We consider problems of sequence processing and propose a solution based on a discrete-state model in order to represent past context. We introduce a recurrent connectionist architecture having a modular structure that associates a subnetwork to each state. The model has a statistical interpretation we call input-output hidden Markov model (IOHMM). It can be trained by the estimation-maximizati...
متن کاملInput / Output HMMs for Sequence
We consider problems of sequence processing and propose a solution based on a discrete state model in order to represent past context. We introduce a recurrent connectionist architecture having a modular structure that associates a subnetwork to each state. The model has a statistical interpretation we call Input/Output Hidden Markov Model (IOHMM). It can be trained by the EM or GEM algorithms,...
متن کاملAn EM Approach to Grammatical Inference: Input/Output HMMs
We propose a modular recurrent connectionist architecture for adaptive temporal processing. The model is given a probabilistic interpretation and is trained using the EM algorithm. This model can also be seen as an Input/Output Hidden Markov Model. The focus of this paper is on sequence classiication tasks. We demonstrate that EM supervised learning is well suited for solving grammatical infere...
متن کاملBoosting Input/Output Hidden Markov Models for Sequence Classification
Input/output hidden Markov model (IOHMM) has turned out to be effective in sequential data processing via supervised learning. However, there are several difficulties, e.g. model selection, unexpected local optima and high computational complexity, which hinder an IOHMM from yielding the satisfactory performance in sequence classification. Unlike previous efforts, this paper presents an ensembl...
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We present a novel approach to learning an HMM whose outputs are distributed according to a parametric family. This is done by decoupling the learning task into two steps: first estimating the output parameters, and then estimating the hidden state transition probabilities. The first step is accomplished by fitting a mixture model to the output stationary distribution. Given the parameters of t...
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ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks
سال: 1996
ISSN: 1045-9227,1941-0093
DOI: 10.1109/72.536317