Results 151 to 160 of about 7,075 (185)

Hidden Semi-Markov Models for Semantic-Graph Language Modeling

Journal of the Franklin Institute
Semantic communication is expected to play a critical role in reducing traffic load in future intelligent large-scale sensor networks. With advances in Machine Learning (ML) and Deep Learning (DL) techniques, design of semantically-aware systems has become feasible in recent years.
Sadik Yagiz Yetim, Tolga M. Duman, Orhan Arikan   +2 more
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Offline and online identification of hidden semi-Markov models

IEEE Transactions on Signal Processing, 2005
We present a new signal model for hidden semi-Markov models (HSMMs). Instead of constant transition probabilities used in existing models, we use state-duration-dependant transition probabilities. We show that our modeling approach leads to easy and efficient implementation of parameter identification algorithms.
Mehran Azimi, Panos Nasiopoulos, Rabab Kreidieh Ward   +2 more
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On efficient Viterbi decoding for hidden semi-Markov models

2008 19th International Conference on Pattern Recognition, 2008
We present algorithms for improved Viterbi decoding for the case of hidden semi-Markov models. By carefully constructing directed acyclic graphs, we pose the decoding problem as that of finding the longest path between specific pairs of nodes. We consider fully connected models as well as restrictive topologies and state duration conditions, and show ...
Ritendra Datta, Jianying Hu, Bonnie K. Ray   +2 more
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Reliability modeling with hidden Markov and semi-Markov chains

2013 IEEE Integration of Stochastic Energy in Power Systems Workshop (ISEPS), 2013
Abstract form only given. Semi-Markov processes and Markov renewal processes represent a class of stochastic processes that generalize Markov and renewal processes. As it is well known, for a discrete-time (respectively continuous-time) Markov process, the sojourn time in each state is geometrically (respectively exponentially) distributed. In the semi-
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Weibull partition models with applications to hidden semi-Markov models

2017 International Joint Conference on Neural Networks (IJCNN), 2017
We develop the Weibull partition model (WPM), which defines a novel nonparametric stochastic process over distributions of partitions of sequential data, aiming at directly modeling the boundaries of segments comprising the sequence. The Weibull partition model employs a Dirichlet process mixture with a Weibull kernel.
Youwei Lu, Shogo Okada, Katsumi Nitta
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A hierarchical hidden semi-Markov model for modeling mobility data

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014
Ubiquity of portable location-aware devices and popularity of online location-based services, have recently given rise to the collection of datasets with high spatial and temporal resolution. The subject of analyzing such data has consequently gained popularity due to numerous opportunities enabled by understanding objects’ (people and animals, among ...
Mitra Baratchi, Nirvana Meratnia, Paul J. M. Havinga, Andrew K. Skidmore, Bert A. G. Toxopeus   +4 more
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Semi-tied covariance matrices for hidden Markov models

IEEE Transactions on Speech and Audio Processing, 1999
There is normally a simple choice made in the form of the covariance matrix to be used with continuous-density HMMs. Either a diagonal covariance matrix is used, with the underlying assumption that elements of the feature vector are independent, or a full or block-diagonal matrix is used, where all or some of the correlations are explicitly modeled ...
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Implementation of hidden semi-Markov models

2011
One of the most frequently used concepts applied to a variety of engineering and scientific studies over the recent years is that of a Hidden Markov Model (HMM). The Hidden semi-Markov model (HsMM) is contrived in such a way that it does not make any premise of constant or geometric distributions of a state duration.
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Hidden Markov and semi-Markov models for count time series

2022
Hidden Markov models (HMMs) are models in which the distributionthat generates an observation depends on the state of an underlying and unobserved Markov process. HMMs have been employed in a variety of areas, including signal processing, bioinformatics, environment and ecology, and are noted for their flexibility and computational efficiency.
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Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series [PDF]

, 2006
Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs) provide flexible, general-purpose models for univariate and multivariate time series. Although interest in HMMs and HSMMs has continuously increased during the past years, and numerous articles on theoretical and practical aspects have been published, several gaps remain.
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