Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Hidden Neural Networks: A Framework for HMM/NN Hybrids
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
IEEE Transactions on Information Theory
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
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In this paper genetic algorithms are used in estimation and decoding processes of a Hidden Markov Model (HMM) and a hybrid HMM/ANN model with conditional binomial distributions. The hybrid model combines a hidden Markov chain with a perceptron which is assumed to constitute a match network. Genetic algorithms are applied here instead of the traditional methods such as the EM algorithm and the Viterbi algorithm. The paper demonstrates performance of an HMM and a hybrid model in modeling the annual number of months, in which some seismic events are recorded. Parameters of the discrete-time two-state models are estimated using the maximum likelihood method, on the basis of data on seismic events that were recorded in Romania in years 1901¨C1990. Then, on the basis of the estimation results, the most likely sequences of states of the hidden Markov chains are found.