Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model

Higher-order Hidden Markov model (HHMM) has a higher prediction accuracy than the first-order Hidden Markov model (HMM). This is due to more exploration of the historical state information for predicting the next state found in HHMM. State sequence for HHMM is invisible but the classical Viterbi...

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Main Author: Chan, Chin Tiong
Format: Thesis
Language:English
Published: 2019
Subjects:
Online Access:http://eprints.usm.my/61146/1/Efficient%20entropy%20based%20decoding%20cut.pdf
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author Chan, Chin Tiong
author_facet Chan, Chin Tiong
author_sort Chan, Chin Tiong
collection USM
description Higher-order Hidden Markov model (HHMM) has a higher prediction accuracy than the first-order Hidden Markov model (HMM). This is due to more exploration of the historical state information for predicting the next state found in HHMM. State sequence for HHMM is invisible but the classical Viterbi algorithm is able to track the optimal state sequence. The extended entropy-based Viterbi algorithm is proposed for decoding HHMM. This algorithm is a memory-efficient algorithm due to its required memory space that is time independent. In other words, the required memory is not subjected to the length of the observational sequence. The entropybased Viterbi algorithm with a reduction approach (EVRA) is also introduced for decoding HHMM. The required memory of this algorithm is also time independent. In addition, the optimal state sequence obtained by the EVRA algorithm is the same as that obtained by the classical Viterbi algorithm for HHMM.
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spelling usm.eprints-611462024-09-18T04:17:06Z http://eprints.usm.my/61146/ Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model Chan, Chin Tiong QA273-280 Probabilities. Mathematical statistics Higher-order Hidden Markov model (HHMM) has a higher prediction accuracy than the first-order Hidden Markov model (HMM). This is due to more exploration of the historical state information for predicting the next state found in HHMM. State sequence for HHMM is invisible but the classical Viterbi algorithm is able to track the optimal state sequence. The extended entropy-based Viterbi algorithm is proposed for decoding HHMM. This algorithm is a memory-efficient algorithm due to its required memory space that is time independent. In other words, the required memory is not subjected to the length of the observational sequence. The entropybased Viterbi algorithm with a reduction approach (EVRA) is also introduced for decoding HHMM. The required memory of this algorithm is also time independent. In addition, the optimal state sequence obtained by the EVRA algorithm is the same as that obtained by the classical Viterbi algorithm for HHMM. 2019-03 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/61146/1/Efficient%20entropy%20based%20decoding%20cut.pdf Chan, Chin Tiong (2019) Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model. PhD thesis, Universiti Sains Malaysia.
spellingShingle QA273-280 Probabilities. Mathematical statistics
Chan, Chin Tiong
Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model
title Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model
title_full Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model
title_fullStr Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model
title_full_unstemmed Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model
title_short Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model
title_sort efficient entropy based decoding algorithms for higher order hidden markov model
topic QA273-280 Probabilities. Mathematical statistics
url http://eprints.usm.my/61146/1/Efficient%20entropy%20based%20decoding%20cut.pdf
work_keys_str_mv AT chanchintiong efficiententropybaseddecodingalgorithmsforhigherorderhiddenmarkovmodel