Results 251 to 260 of about 14,908,682 (295)
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Partially hidden Markov models
IEEE Transactions on Information Theory, 1996Summary: Partially hidden Markov models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression where the hidden variables may be interpreted as ...
Søren Forchhammer, Jorma Rissanen
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Ergodicity of hidden Markov models
Mathematics of Control, Signals, and Systems, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Giovanni B. Di Masi, Lukasz Stettner
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Hidden Markov Models in Bioinformatics
Current Bioinformatics, 2007Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks.
Valeria De Fonzo +2 more
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Filtering on hidden Markov models
IEEE Signal Processing Letters, 2000In this letter, we propose a novel approach to adapt the hidden Markov model (HMM) parameters when the original feature vector sequences are transformed by a causal finite impulse response (FIR) filter. Our approach enables us to be free from the requirement of retraining the whole recognition parameters when the feature vectors are changed and makes ...
Nam Soo Kim, Dong Kook Kim
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Contextual Hidden Markov Models
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012Multiple works have proposed extensions of HMMs for handling variability. We focus here on the design of HMMs whose probability distribution on sequences depends on additional external variables that we call the context, which may stand for emotion features in speech recognition, physical features in gesture recognition, etc. We show experimentally the
Radenen, Mathieu, Artières, Thierry
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Current Opinion in Structural Biology, 1996
'Profiles' of protein structures and sequence alignments can detect subtle homologies. Profile analysis has been put on firmer mathematical ground by the introduction of hidden Markov model (HMM) methods. During the past year, applications of these powerful new HMM-based profiles have begun to appear in the fields of protein-structure prediction and ...
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'Profiles' of protein structures and sequence alignments can detect subtle homologies. Profile analysis has been put on firmer mathematical ground by the introduction of hidden Markov model (HMM) methods. During the past year, applications of these powerful new HMM-based profiles have begun to appear in the fields of protein-structure prediction and ...
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Linguistic hidden Markov models
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03., 2004In this paper we develop a hidden Markov model (HMM), called the linguistic HMM (LHMM), suitable for processing sequences of fuzzy vectors. A fuzzy vector B is an n-tuple of fuzzy numbers. Since fuzzy numbers are often associated with linguistic terms, such as "small," "medium," etc., a fuzzy vector can also be called a linguistic vector.
Mihail Popescu +2 more
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A Hidden Markov Model for Seismocardiography
IEEE Transactions on Biomedical Engineering, 2017We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate,
Johan Wahlström +6 more
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Nonstationary hidden Markov model
Signal Processing, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
SIN, B, KIM, JH Kim, JinHyung
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2019
Hidden Markov models are tractable to capture long-term dependencies but intractable to compute the transition probabilities of higher-order process. We propose a neural hidden Markov models to compute the transition probabilities of higher-order hidden Markov model by a neural network and reduce the cost of computation.
Zuoquan Lin, Jiehu Song
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Hidden Markov models are tractable to capture long-term dependencies but intractable to compute the transition probabilities of higher-order process. We propose a neural hidden Markov models to compute the transition probabilities of higher-order hidden Markov model by a neural network and reduce the cost of computation.
Zuoquan Lin, Jiehu Song
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