Results 201 to 210 of about 8,159 (239)
Some of the next articles are maybe not open access.
Revisiting semi-continuous hidden Markov models
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012In the past decade, semi-continuous hidden Markov models (SCHMMs) have not attracted much attention in the speech recognition community. Growing amounts of training data and increasing sophistication of model estimation led to the impression that continuous HMMs are the best choice of acoustic model.
Korbinian Riedhammer +3 more
openaire +1 more source
Semi-hidden Markov models for generation and analysis of sequences
Mathematics and Computers in Simulation, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
R. Román-Gálvez +3 more
openaire +2 more sources
Modeling state duration and emission dependence in hidden Markov and hidden semi-Markov models
2023Hidden Markov models (HMM) are composed of a latent state sequence and an observation sequence conditionally independent on the states, which follows an emission distribution. Hidden semi-Markov models (HSMM) extend the HMM by explicitly modeling the duration in the states.
openaire +2 more sources
Hidden semi-Markov models for electricity load disaggregation
ACM SIGMETRICS Performance Evaluation Review, 2019This paper assesses the performance of a technique for estimating the power consumption of individual devices based on aggregate consumption. The new semi-Markov technique, outperforms pure hidden Markov models on the REDD dataset. The technique also exploits information from transients to eliminate a substantial fraction of the observed ...
Yung Fei Wong +2 more
openaire +1 more source
Hidden Semi-Markov Models for Semantic-Graph Language Modeling
Journal of the Franklin InstituteSemantic 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 +2 more
openaire +2 more sources
Offline and online identification of hidden semi-Markov models
IEEE Transactions on Signal Processing, 2005We 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 +2 more
openaire +1 more source
On efficient Viterbi decoding for hidden semi-Markov models
2008 19th International Conference on Pattern Recognition, 2008We 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 +2 more
openaire +1 more source
Reliability modeling with hidden Markov and semi-Markov chains
2013 IEEE Integration of Stochastic Energy in Power Systems Workshop (ISEPS), 2013Abstract 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-
openaire +1 more source
Weibull partition models with applications to hidden semi-Markov models
2017 International Joint Conference on Neural Networks (IJCNN), 2017We 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
openaire +1 more source
A hierarchical hidden semi-Markov model for modeling mobility data
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014Ubiquity 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 +4 more
openaire +2 more sources