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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my-usm-ep.611462024-09-18T04:17:06Z Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model 2019-03 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 http://eprints.usm.my/61146/ http://eprints.usm.my/61146/1/Efficient%20entropy%20based%20decoding%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Matematik |
| institution |
Universiti Sains Malaysia |
| collection |
USM Institutional Repository |
| language |
English |
| topic |
QA273-280 Probabilities Mathematical statistics |
| spellingShingle |
QA273-280 Probabilities Mathematical statistics Chan, Chin Tiong Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model |
| 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. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Chan, Chin Tiong |
| author_facet |
Chan, Chin Tiong |
| author_sort |
Chan, Chin Tiong |
| title |
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_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_sort |
efficient entropy-based decoding
algorithms for higher-order hidden
markov model |
| granting_institution |
Universiti Sains Malaysia |
| granting_department |
Pusat Pengajian Sains Matematik |
| publishDate |
2019 |
| url |
http://eprints.usm.my/61146/1/Efficient%20entropy%20based%20decoding%20cut.pdf |
| _version_ |
1811772886026813440 |