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New trends in Markov models and related learning to restore data

Florence Forbes, Wojciech Pieczynski
2009 2009 IEEE International Workshop on Machine Learning for Signal Processing  
These capabilities are illustrated in the cited published works and more recently in the contributions to the Special Session on Markov models of the IEEE International Workshop on Machine Learning for  ...  We present recent approaches that extend standard Markov models and increase their modelling power.  ...  HMTs were originally proposed in [25] to detect changes in binary trees associated to wavelet coefficients for signal processing applications.  ... 
doi:10.1109/mlsp.2009.5306255 fatcat:qmvfa6iosnbw5p4c6lmyycv2mq

Connected image processing with multivariate attributes: An unsupervised Markovian classification approach

B. Perret, Ch. Collet
2015 Computer Vision and Image Understanding  
It relies on a hierarchical Markovian unsupervised algorithm in order to classify the nodes of the traditional Max-Tree.  ...  This article provides also a new insight in the field of hierarchical Markovian image processing showing that morphological trees can advantageously replace traditional quadtrees.  ...  The structure of the Markovian model is exactly the one of the Max-Tree. 370message passing algorithm[63].  ... 
doi:10.1016/j.cviu.2014.09.008 fatcat:movjssgxmjdivabag3md7t4nhi

Dynamical Systems Trees [article]

Andrew Howard, Tony S. Jebara
2012 arXiv   pre-print
We propose dynamical systems trees (DSTs) as a flexible class of models for describing multiple processes that interact via a hierarchy of aggregating parent chains.  ...  We provide tractable inference and learning algorithms for arbitrary DST topologies via an efficient structured mean-field algorithm.  ...  Acknowledgements We would like to thank Anshul Kundaje and Darrin Lewis for helpful discussions on gene expression levels. This research is funded in part by grant IIS-0347499 from the NSF.  ... 
arXiv:1207.4148v1 fatcat:mhkyn5hka5hnrlqncns52cyfh4

Discovering the Hidden Structure of Complex Dynamic Systems [article]

Xavier Boyen, Nir Friedman, Daphne Koller
2013 arXiv   pre-print
Learning provides an alternative approach for constructing models of dynamic systems.  ...  Our approach is based on the Structural Expectation Maximization (SEM) algorithm. The main computational cost of the SEM algorithm is the gathering of expected sufficient statistics.  ...  This function measures the extent to which the data is likely given a candidate model B; it is thus an estimate of how well a given candidate fits the empirical data.  ... 
arXiv:1301.6683v1 fatcat:btv3ihxkhrgifhfnmkngvm6bzm

Benchmarking of Remote Sensing Segmentation Methods

Stanislav Mikes, Michal Haindl, Giuseppe Scarpa, Raffaele Gaetano
2015 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The meaningfulness of the newly proposed dataset is demonstrated by testing and comparing several RS segmentation algorithms, and showing that the benchmark figures provide a solid framework for the fair  ...  We present the enrichment of the Prague Texture Segmentation Data-Generator and Benchmark (PTSDB) to include the assessment of the remote sensing (RS) image segmenters.  ...  Since 2002, he has been a Researcher with the Department of Pattern Recognition, Institute of Information Theory and Automation, Czech Academy of Sciences, Prague, Czech Republic.  ... 
doi:10.1109/jstars.2015.2416656 fatcat:u32o3xpug5fv5kua772sovub5e

Wavelet-based statistical signal processing using hidden Markov models

M.S. Crouse, R.D. Nowak, R.G. Baraniuk
1998 IEEE Transactions on Signal Processing  
Efficient expectation maximization algorithms are developed for fitting the HMM's to observational signal data.  ...  To demonstrate the utility of wavelet-domain HMM's, we develop novel algorithms for signal denoising, classification, and detection.  ...  To fit an HMT to the noisy wavelet coefficients, we apply the EM algorithm from the Appendix.  ... 
doi:10.1109/78.668544 fatcat:agm3p7vbkjfardbtqrara3wn64

Graphical Models: Foundations of Neural Computation

Michael I. Jordan, Terrence J. Sejnowski
2002 Pattern Analysis and Applications  
A graphical model has both a str~lctural con~ponent-encoded L7p the pattern oI edges in the gsapl1-and a paramctr-ic compo~ient-encoded by ~iumcl-ical "potentials" associated with sets of cdgcs in thc  ...  The selationship between these components underlies the computational machincq~ associated with graphical models.  ...  Jordan and Jacobs also proposed using the EM algorithm to fit the parameters of mixture-of-experts architectures.  ... 
doi:10.1007/s100440200036 fatcat:bt75wlwba5hefifledkf62lv4e

Wavelet-based Image Modelling for Compression Using Hidden Markov Model

Muhammad Usman, Imran Touqir, Maham Haider
2016 International Journal of Advanced Computer Science and Applications  
To estimate the parameter of wavelet based Hidden Markov model, an efficient expectation maximization algorithm is developed.  ...  Statistical signal modeling using hidden Markov model is one of the techniques used for image compression.  ...  Expectation Maximization algorithm for training In EM algorithm, "Training" is used for fitting the model parameters to the Wavelet based hidden Markov model.  ... 
doi:10.14569/ijacsa.2016.071139 fatcat:2miq2utjorcwnl5zeu45oo4no4

Compositional Generative Mapping for Tree-Structured Data—Part II: Topographic Projection Model

D. Bacciu, A. Micheli, A. Sperduti
2013 IEEE Transactions on Neural Networks and Learning Systems  
We introduce GTM-SD, the first compositional generative model for topographic mapping of tree-structured data.  ...  "Compositional Generative Mapping for Tree-Structured Data -Part I: Bottom-Up Probabilistic Modeling of Trees", IEEE Trans. on Neural Netw. and Learn.  ...  ACKNOWLEDGMENT The authors would like to thank Nikolaos Gianniotis, for having provided experimental data and pictures from [7] , and Giovanni Da San Martino, for his support with the SOM-SD experiments  ... 
doi:10.1109/tnnls.2012.2228226 pmid:24808278 fatcat:k3fwnsyc55hntmbyf7wwmjbmvu

Online Bayesian tree-structured transformation of HMMs with optimal model selection for speaker adaptation

Shaojun Wang, Yunxin Zhao
2001 IEEE Transactions on Speech and Audio Processing  
To balance between model complexity and goodness of fit to adaptation data, a dynamic programming algorithm is developed for selecting models using a Bayesian variant of the "minimum description length  ...  By constructing a clustering tree of HMM Gaussian mixture components, the linear regression (LR) or affine transformation parameters for HMM Gaussian mixture components are dynamically searched.  ...  Mokbel for providing their preprints. Finally, they thank J. J. Liu of Beckman Institute, University of Illinois at Urbana-Champaign, for a helpful discussion about Bayesian model selection.  ... 
doi:10.1109/89.943344 fatcat:wqakgiaxrjdcnovxeql5ulnzwq

Distributed Markovian segmentation: Application to MR brain scans

Nathalie Richard, Michel Dojat, Catherine Garbay
2007 Pattern Recognition  
A situated approach to Markovian image segmentation is proposed based on a distributed, decentralized and cooperative strategy for model estimation.  ...  According to this approach, the EM-based model estimation is performed locally to cope with spatially varying intensity distributions, as well as non-homogeneities in the appearance of objects.  ...  The Markovian field z is then approximated at each iteration of the EM algorithm with an independent variable systemz = {z 1 , . . .  ... 
doi:10.1016/j.patcog.2007.03.019 fatcat:6buuhr2wozelbjldth73wx4pxy

Global Analysis of the Gravitational Wave Signal from Galactic Binaries [article]

Tyson Littenberg, Neil Cornish, Kristen Lackeos, Travis Robson
2020 arXiv   pre-print
that simultaneously fits for all signals in the band.  ...  Typical galactic binaries are millions of years from merger, and consequently their signals will persist for the the duration of the LISA mission.  ...  Thorpe for insightful discussions during the development of this pipeline, particularly related to the catalog development.  ... 
arXiv:2004.08464v1 fatcat:crybqbzwczdndi7evejatpo3ne

Analysis of dynamic algorithms in Knuth's model

J. Françon, B. Randrianarimanana, R. Schott
1990 Theoretical Computer Science  
the following assumptions: if the size of the data structure is k (k E N), then the number of possibilities for the operations D and Q' is a linear function of k, whereas the number of possibilities for  ...  This p-lper analyzes the average behaviour of algorithms that operate on dynamically varying data structures subject to insertions 1, deletions D, positive (resp. negative) queries Q' (resp. 0-j under  ...  The first answers for linear lists an rity queues were given in [8, 15] , after reducing the calculations in Knuth's model to calculations in the markovian model.  ... 
doi:10.1016/0304-3975(90)90033-e fatcat:n2b7htvhg5frzhvausyhaoxyvi

A hybrid model for predicting human physical activity status from lifelogging data

Ji Ni, Bowei Chen, Nigel M. Allinson, Xujiong Ye
2019 European Journal of Operational Research  
It fits seamlessly with the current trend in the UK healthcare transformation of patient empowerment as well as contributing to a strategic development for more efficient and cost-effective provision of  ...  The model has a two-stage hybrid structure (in short, MOGP-HMM) -- a multi-objective genetic programming (MOGP) algorithm in the first stage to reduce the dimensions of lifelogging data and a hidden Markov  ...  Acknowledgment This work was conducted with the support of the EPSRC grant MyLifeHub EP/L023679/1 and European FP7 collaborative project MyHealthAvatar (GA No: 600929).  ... 
doi:10.1016/j.ejor.2019.05.035 fatcat:aunqailq25fdnoz2hamxn6igw4

Spectral Machine Learning for Predicting Power Wheelchair Exercise Compliance [chapter]

Robert Fisher, Reid Simmons, Cheng-Shiu Chung, Rory Cooper, Garrett Grindle, Annmarie Kelleher, Hsinyi Liu, Yu Kuang Wu
2014 Lecture Notes in Computer Science  
The second, a decision tree using information gain, is computationally efficient and produces an output that is easy for clinicians and wheelchair users to understand.  ...  We present two models of compliance prediction. The first, a spectral Hidden Markov Model, uses fast, optimal optimization techniques to train a sequential classifier.  ...  We also acknowledge the Pittsburgh chapter of the American Rewards for College Scientists (ARCS) program for their generous support.  ... 
doi:10.1007/978-3-319-08326-1_18 fatcat:fwvwc6h5wzgdzluv5rpeem2liu
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