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SENSITIVITY OF HIDDEN MARKOV MODELS
 APPLIED PROBABILITY TRUST
, 2005
"... We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the ini ..."
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Cited by 4 (2 self)
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We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix
Convex Hidden Markov Models
"... We present a new unsupervised algorithm for training hidden Markov models that is convex and avoids the use of EM. The idea is to formulate an unsupervised version of maximum margin Markov networks that can be trained via semidefinite programming. The result is a discriminative training criterion fo ..."
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Cited by 1 (0 self)
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We present a new unsupervised algorithm for training hidden Markov models that is convex and avoids the use of EM. The idea is to formulate an unsupervised version of maximum margin Markov networks that can be trained via semidefinite programming. The result is a discriminative training criterion
Hidden Markov Models
, 2004
"... The Hidden Markov Model (HMM) is a popular statistical tool for modelling a wide range of time series data. In the context of natural language processing(NLP), HMMs have been applied with great success to problems such as partofspeech tagging and nounphrase chunking. 1 ..."
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Cited by 6 (0 self)
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The Hidden Markov Model (HMM) is a popular statistical tool for modelling a wide range of time series data. In the context of natural language processing(NLP), HMMs have been applied with great success to problems such as partofspeech tagging and nounphrase chunking. 1
Hidden Markov Models and Their Mixtures
 DEA Report, Dep. of Mathematics, Universit�� catholique de Louvain
, 1996
"... Hidden Markov Models and Their Mixtures by Christophe Couvreur Diplome d"etudes approfondies en math'ematiques Facult'e des sciences  D'epartement de math'ematiques, Universit'e catholique de Louvain Prof. JeanMarie Rolin, Advisor Hidden Markov models (HMMs) form a ..."
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Hidden Markov Models and Their Mixtures by Christophe Couvreur Diplome d"etudes approfondies en math'ematiques Facult'e des sciences  D'epartement de math'ematiques, Universit'e catholique de Louvain Prof. JeanMarie Rolin, Advisor Hidden Markov models (HMMs) form a
PartiallyHidden Markov Models
 In International
, 2012
"... Abstract This paper addresses the problem of Hidden Markov Models (HMM) training and inference when the training data are composed of feature vectors plus uncertain and imprecise labels. The “soft ” labels represent partial knowledge about the possible states at each time step and the “softness ” is ..."
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Cited by 1 (1 self)
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Abstract This paper addresses the problem of Hidden Markov Models (HMM) training and inference when the training data are composed of feature vectors plus uncertain and imprecise labels. The “soft ” labels represent partial knowledge about the possible states at each time step and the “softness
The Hierarchical Hidden Markov Model: Analysis and Applications
 MACHINE LEARNING
, 1998
"... . We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our model is motivated by the complex multiscale structure which appears in many natural sequences, particularly in langua ..."
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Cited by 326 (3 self)
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. We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our model is motivated by the complex multiscale structure which appears in many natural sequences, particularly
Hidden Markov Model Regression
, 1993
"... Hidden Markov Model Regression (HMMR) is an extension of the Hidden Markov Model (HMM) to regression analysis. We assume that the parameters of the regression model are determined by the outcome of a finitestate Markov chain and that the error terms are conditionally independent normally distribute ..."
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Cited by 9 (0 self)
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Hidden Markov Model Regression (HMMR) is an extension of the Hidden Markov Model (HMM) to regression analysis. We assume that the parameters of the regression model are determined by the outcome of a finitestate Markov chain and that the error terms are conditionally independent normally
A tutorial on hidden Markov models and selected applications in speech recognition
 PROCEEDINGS OF THE IEEE
, 1989
"... Although initially introduced and studied in the late 1960s and early 1970s, statistical methods of Markov source or hidden Markov modeling have become increasingly popular in the last several years. There are two strong reasons why this has occurred. First the models are very rich in mathematical s ..."
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Cited by 5892 (1 self)
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Although initially introduced and studied in the late 1960s and early 1970s, statistical methods of Markov source or hidden Markov modeling have become increasingly popular in the last several years. There are two strong reasons why this has occurred. First the models are very rich in mathematical
Hidden Markov Models
"... Hidden Markov chains was originally introduced and studied in the late 1960s and early 1970s. During the 1980s the models became increasingly popular. The reason for this is twofolded. Firstly, the hidden Markov models are very rich in mathematical structure and hence can form the theoretical basis ..."
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Hidden Markov chains was originally introduced and studied in the late 1960s and early 1970s. During the 1980s the models became increasingly popular. The reason for this is twofolded. Firstly, the hidden Markov models are very rich in mathematical structure and hence can form the theoretical basis
Constrained hidden Markov models
 In Solla et al. (2000
"... By thinking of each state in a hidden Markov model as corresponding to some spatial region of a fictitious topology space it is possible to naturally define neighbouring states as those which are connected in that space. The transition matrix can then be constrained to allow transitions only betwee ..."
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Cited by 23 (2 self)
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By thinking of each state in a hidden Markov model as corresponding to some spatial region of a fictitious topology space it is possible to naturally define neighbouring states as those which are connected in that space. The transition matrix can then be constrained to allow transitions only
Results 11  20
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