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Introduction to Computational Thinking, 2021
Some temporal patterns are difficult to detect, and to learn, because they are hidden: only indirect clues are telling us what is going on under the surface. Problems of this kind fall under the rubric of Hidden Markov Models, HMM.
Thomas Mailund
semanticscholar +5 more sources
Some temporal patterns are difficult to detect, and to learn, because they are hidden: only indirect clues are telling us what is going on under the surface. Problems of this kind fall under the rubric of Hidden Markov Models, HMM.
Thomas Mailund
semanticscholar +5 more sources
Markov models — hidden Markov models
Nature Methods, 2019“Everything we see hides another thing, we always want to see what is hidden by what we see” — Rene ...
Jasleen K. Grewal +2 more
openaire +2 more sources
2001
We divide this brief account of hidden Markov models into three sections: (i) a description of the properties of these models, (ii) the three main algorithms of the models, (iii) applications. For a more complete account of these models, see Rabiner (1989).
Warren J. Ewens, Gregory R. Grant
+4 more sources
We divide this brief account of hidden Markov models into three sections: (i) a description of the properties of these models, (ii) the three main algorithms of the models, (iii) applications. For a more complete account of these models, see Rabiner (1989).
Warren J. Ewens, Gregory R. Grant
+4 more sources
Real-Time Multistep Attack Prediction Based on Hidden Markov Models
IEEE Transactions on Dependable and Secure Computing, 2020A novel method based on the Hidden Markov Model is proposed to predict multistep attacks using IDS alerts. We consider the hidden states as similar phases of a particular type of attack.
Pilar Holgado +2 more
semanticscholar +1 more source
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 ...
openaire +3 more sources
2011
Hidden Markov models (HMMs) are important in pattern recognition because they are ideally suited to classify patterns where each pattern is made up of a sequence of sub-patterns. For example, assume that a day is either sunny, cloudy, or rainy corresponding to three different types of weather conditions.
M. Narasimha Murty, V. Susheela Devi
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Hidden Markov models (HMMs) are important in pattern recognition because they are ideally suited to classify patterns where each pattern is made up of a sequence of sub-patterns. For example, assume that a day is either sunny, cloudy, or rainy corresponding to three different types of weather conditions.
M. Narasimha Murty, V. Susheela Devi
+4 more sources
Revisiting Hidden Markov Models for Speech Emotion Recognition
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019Hidden Markov models (HMMs) have a long tradition in automatic speech recognition (ASR) due to their capability of capturing temporal dynamic characteristics of speech.
Shuiyang Mao +4 more
semanticscholar +1 more source
2010
This book build upon the use of Hidden Markov Models as motion models, which, as we have seen in chapter 3, are probably the most popular technique for pattern based motion prediction. This chapter provides the reader with a broad introduction to this probabilistic framework.
Sergios Theodoridis +3 more
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This book build upon the use of Hidden Markov Models as motion models, which, as we have seen in chapter 3, are probably the most popular technique for pattern based motion prediction. This chapter provides the reader with a broad introduction to this probabilistic framework.
Sergios Theodoridis +3 more
openaire +2 more sources