A Novel Entropy-Based Decoding Algorithm for a Generalized High-Order Discrete Hidden Markov Model
المؤلفون المشاركون
Ong, Hong Choon
Chan, Jason Chin-Tiong
المصدر
Journal of Probability and Statistics
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-05-02
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
The optimal state sequence of a generalized High-Order Hidden Markov Model (HHMM) is tracked from a given observational sequence using the classical Viterbi algorithm.
This classical algorithm is based on maximum likelihood criterion.
We introduce an entropy-based Viterbi algorithm for tracking the optimal state sequence of a HHMM.
The entropy of a state sequence is a useful quantity, providing a measure of the uncertainty of a HHMM.
There will be no uncertainty if there is only one possible optimal state sequence for HHMM.
This entropy-based decoding algorithm can be formulated in an extended or a reduction approach.
We extend the entropy-based algorithm for computing the optimal state sequence that was developed from a first-order to a generalized HHMM with a single observational sequence.
This extended algorithm performs the computation exponentially with respect to the order of HMM.
The computational complexity of this extended algorithm is due to the growth of the model parameters.
We introduce an efficient entropy-based decoding algorithm that used reduction approach, namely, entropy-based order-transformation forward algorithm (EOTFA) to compute the optimal state sequence of any generalized HHMM.
This EOTFA algorithm involves a transformation of a generalized high-order HMM into an equivalent first-order HMM and an entropy-based decoding algorithm is developed based on the equivalent first-order HMM.
This algorithm performs the computation based on the observational sequence and it requires OTN~2 calculations, where N~ is the number of states in an equivalent first-order model and T is the length of observational sequence.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chan, Jason Chin-Tiong& Ong, Hong Choon. 2018. A Novel Entropy-Based Decoding Algorithm for a Generalized High-Order Discrete Hidden Markov Model. Journal of Probability and Statistics،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1197701
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chan, Jason Chin-Tiong& Ong, Hong Choon. A Novel Entropy-Based Decoding Algorithm for a Generalized High-Order Discrete Hidden Markov Model. Journal of Probability and Statistics No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1197701
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chan, Jason Chin-Tiong& Ong, Hong Choon. A Novel Entropy-Based Decoding Algorithm for a Generalized High-Order Discrete Hidden Markov Model. Journal of Probability and Statistics. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1197701
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1197701
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر