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Temporal Data Mining Using Hidden Markov-Local Polynomial Models [chapter]

Weiqiang Lin, Mehmet A. Orgun, Graham J. Williams
2001 Lecture Notes in Computer Science  
polynomial analysis; and then the third level based on hidden Markov-local polynomial models (HMLPMs), finds global patterns from a DTS set.  ...  At the first level, a structuralbased search based on distance measure models is employed to find pattern structures; the second level performs a value-based search on the discovered patterns using local  ...  Our proposed framework is based on a new model for discovering patterns by using hidden Markov models and local polynomial modelling.  ... 
doi:10.1007/3-540-45357-1_35 fatcat:drfuyz477ngndheal4xg3hyhei

Estimation of Switched Markov Polynomial NARX models [article]

Alessandro Brusaferri and Matteo Matteucci and Stefano Spinelli
2020 arXiv   pre-print
This work targets the identification of a class of models for hybrid dynamical systems characterized by nonlinear autoregressive exogenous (NARX) components, with finite-dimensional polynomial expansions  ...  Discrete mode classification and NARX regression tasks are disentangled within the iterations.  ...  To this end, the discrete state evolution can be modeled by Markov chains, leading to Jump Markov Systems [10] .  ... 
arXiv:2009.14073v1 fatcat:3evbp2t6jndnnic26qqx52wr74

Hybrid Model Based Sampling Algorithm to Infer Dynamic Complex Network

Jin Guo
2017 International Journal of Performability Engineering  
In this letter, a new Hybrid Model based Latent Variables Sampling algorithm is presented to address the problems of high computation complexity and low accuracy faced by traditional approaches.  ...  Hidden Markov model This paper adopts the Hidden Markov model to describe successive network structural changes, state transitions of each phase.  ...  As for change-points, the starting position of a phase, discrete hidden variable  is introduced, the dimension of which equals K .  ... 
doi:10.23940/ijpe.17.02.p12.231239 fatcat:nmshfovxrvdzvcoe6rs7nefc2q

HMM-guided frame querying for bandwidth-constrained video search [article]

Bhairav Chidambaram, Mason McGill, Pietro Perona
2019 arXiv   pre-print
Using a convolutional neural network to score individual frames and a hidden Markov model to propagate predictions across frames, our agent accurately identifies temporal regions of interest based on sparse  ...  We design an agent to search for frames of interest in video stored on a remote server, under bandwidth constraints.  ...  We solve (2) using a hidden Markov model (HMM) derived from the transition and co-ocurrence statistics of ground-truth frame labels and regressed frame scores.  ... 
arXiv:2001.00057v1 fatcat:46aubumeqnde5i5hzpbikidwna

Challenges in Transition to m Commerce in Rural India

Nishi Malhotra, Pankaj Shah, Saravanan S.
2017 International Journal of Computer Applications  
The study concludes that Hidden Markov Model based on Multinomial Logit Regression approach is the best model to study the given problem.  ...  An descriptive study to evaluate various kinds of models for different kinds of data distribution is aimed at identifying the best kind of Hidden Markov Model for studying the issue of channel migration  ...  The Hidden Markov Model based on Multinomial Logit Regression is proposed to capture the channel choice behavior of the customers in Tier II and Tier III cities in India, based on secondary and primary  ... 
doi:10.5120/ijca2017915387 fatcat:wg2lwtqisrhupcbampwigsotpi

Inference, Prediction, and Entropy-Rate Estimation of Continuous-time, Discrete-event Processes [article]

S. E. Marzen, J. P. Crutchfield
2020 arXiv   pre-print
However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them.  ...  Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground.  ...  ACKNOWLEDGMENTS This material is based upon work supported by, or in part by, the U. S. Army Research Laboratory and the U. S.  ... 
arXiv:2005.03750v1 fatcat:fwxzmon3zzep7ojzm3bcjmztsm

Efficient Nonlinear Markov Models for Human Motion

Andreas M. Lehrmann, Peter V. Gehler, Sebastian Nowozin
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition  
Dynamic Bayesian networks such as Hidden Markov Models (HMMs) are successfully used as probabilistic models for human motion.  ...  The use of hidden variables makes them expressive models, but inference is only approximate and requires procedures such as particle filters or Markov chain Monte Carlo methods.  ...  Latent space view and comparison to Hidden Markov Models In this section we will compare our Dynamic Forest Model with the class of Hidden Markov Models.  ... 
doi:10.1109/cvpr.2014.171 dblp:conf/cvpr/LehrmannGN14 fatcat:h3dbevht5vbhlki7tuqeehpxl4

Advances in neural information processing systems 9: Proceedings of the 1996 conference

1998 Computers and Mathematics with Applications  
Training algorithms for hidden Markov models using entropy based distance functions (Yoram Singer and Manfred K. Warmuth). Clustering sequences with hidden Markov models (Padhralc Smyth).  ...  A variational principle for model-based morphing (Lawrence K. Saul and Michael I. Jordan). Online learning from finite training sets: An analytical case study (Peter Sollich and David Barber) .  ... 
doi:10.1016/s0898-1221(98)90499-0 fatcat:cwqgiuxzkrfqppnri7kzvpy4um

Quantile regression for longitudinal data: unobserved heterogeneity and informative missingness [article]

Maria Francesca Marino and Nikos Tzavidis and Marco Alfo'
2015 arXiv   pre-print
To deal with the case of irretrievable drop-out, we introduce a pattern mixture version of the linear quantile hidden Markov model, where we account for time-varying heterogeneity and for changes in the  ...  In this manuscript, we introduce a general quantile regression model for longitudinal, continuous, responses where time-varying and time-constant random parameters are jointly taken into account.  ...  Concluding remarks In this manuscript we have discussed a class of mixed hidden Markov quantile regression models for longitudinal continuous responses; a general dependence structure is considered by  ... 
arXiv:1501.02157v2 fatcat:qds3uvyiifhxvb6indxlbk67wq

Statistical Analysis of Different Artificial Intelligent Techniques applied to Intrusion Detection System

Hind Tribak, Olga Valenzuela, Fernando Rojas, Ignacio Rojas
2022 International Journal of Systems Applications, Engineering & Development  
learning algorithms on NSL-KDD data set, to recognize between normal and attack connections and compare their performing in different scenariosdiscretization, features selections and algorithm method for  ...  A discrete hidden Markov model is defined in terms of the following elements [24] IV.  ...  Hidden Markov Models Introduced by L. E. Baum in the 70's [23] , Baum proposes this model as a statistical method of estimation of probabilistic functions of a Markov chain.  ... 
doi:10.46300/91015.2022.16.10 fatcat:oftrbmokjrhqtetujavzwak4se

What HMMs Can Do

2006 IEICE transactions on information and systems  
This paper concludes that, in search of a model to supersede the HMM (say for ASR), rather than trying to correct for HMM limitations in the general case, new models should be found based on their potential  ...  Since their inception almost fifty years ago, hidden Markov models (HMMs) have have become the predominant methodology for automatic speech recognition (ASR) systems -today, most state-of-the-art speech  ...  E-mail: <> Definition 3. 1 . 1 Hidden Markov Model A hidden Markov model (HMM) is collection of random variables consisting of a set of T discrete scalar variables Q 1:T and a  ... 
doi:10.1093/ietisy/e89-d.3.869 fatcat:bk5nrxnnyjhafhd7prwjezm3pi

Learning Hidden Markov Models for Regression using Path Aggregation

Keith Noto, Mark Craven
2008 Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence  
We present and evaluate an approach to learning a type of hidden Markov model (HMM) for regression.  ...  The learning process involves inferring the structure and parameters of a conventional HMM, while simultaneously learning a regression model that maps features that characterize paths through the model  ...  The authors would like to thank Audrey Gasch, Yue Pan and Tim Durfee for help with data and analysis.  ... 
pmid:21785575 pmcid:PMC3141580 fatcat:nvahfgw7rbbrviubi5zs6e4pru

Hybrid system identification using a mixture of NARX experts with LASSO-based feature selection

Alessandro Brusaferri, Matteo Matteucci, Pietro Portolani, Stefano Spinelli, Andrea Vitali
2020 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)  
Despite being conceived to process static data, MoE models have been exploited also for the identification of non-stationary time series, including input output Hidden Markov Models [10] .  ...  In this work, we target Switching Nonlinear Auto-Regressive with Exogeneous inputs (SNARX) systems, representing a broad class of hybrid problems, including CPS.  ...  Despite being conceived to process static data, MoE models have been exploited also for the identification of non-stationary time series, including input output Hidden Markov Models [10] .  ... 
doi:10.1109/codit49905.2020.9263962 fatcat:qf76gbnpyzd6bcw6j7tg5auc6e

Motion-Sound Mapping through Interaction

Jules Françoise, Frédéric Bevilacqua
2018 ACM transactions on interactive intelligent systems (TiiS)  
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not.  ...  We thank our collaborators for the Vocalization project, in particular Norbert Schnell and Riccardo Borghesi, and for the SoundGuides project: Olivier Chapuis and Sylvain Hanneton.  ...  This work was supported by the EDITE school for doctoral studies at Université Pierre et Marie Curie, by the LEGOS project (ANR Grant 31639884), by the Rapid-Mix EU project (H2020-ICT-2014-1 Project ID  ... 
doi:10.1145/3211826 fatcat:nmz2a6o2ffhk3htrwscwpcmfny

Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models

Rita Justo-Silva, Adelino Ferreira, Gerardo Flintsch
2021 Sustainability  
Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future.  ...  This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature.  ...  Acknowledgments: The author Rita Justo-Silva is grateful to the Portuguese Foundation for Science and Technology for her MIT-Portugal grant (PD/BD/113721/2015).  ... 
doi:10.3390/su13095248 fatcat:rdfr37loirgzdmzhmbckldlat4
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