Factorial Fractional Hidden Markov Models
Conventional hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive Gaussian noise. A lesser known extension of this class of models, is the so called Factorial Hidden Model (FHMM). FHMM’s also have numerous applications, notably in machine learn...
Збережено в:
| Дата: | 2012 |
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| Автор: | |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
Інститут проблем моделювання в енергетиці ім. Г.Є. Пухова НАН України
2012
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| Назва видання: | Электронное моделирование |
| Теми: | |
| Онлайн доступ: | http://dspace.nbuv.gov.ua/handle/123456789/61826 |
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| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Цитувати: | Factorial Fractional Hidden Markov Models / L. Aggoun // Электронное моделирование. — 2012 — Т. 34, № 3. — С. 59-67. — Бібліогр.: 11 назв. — англ. |
Репозитарії
Digital Library of Periodicals of National Academy of Sciences of Ukraine| Резюме: | Conventional hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive Gaussian noise. A lesser known extension of this class of models, is the so called Factorial Hidden Model (FHMM). FHMM’s also have numerous applications, notably in machine learning and speech recognition. In this article we consider FHMM’s with additive fractional Gaussian noise in the observed process. |
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