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Expectation–maximization algorithm
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#	Item
711	Daozheng Chen 250 W El Camino Real, APT#4111, Sunnyvale, CA[removed]Phone: ([removed]E-Mail: [removed] Research Interests • Add to Reading List Source URL: www.cs.umd.edu Language: English - Date: 2013-08-01 03:00:54 Theoretical computer science Machine learning Statistical models Statistical classification Conditional random field Support vector machine MATLAB Expectation–maximization algorithm Algorithm Statistics Applied mathematics Mathematics
712	Variational Bayesian Image Modelling Li Cheng Department of Computing Science, University of Alberta, Canada Feng Jiao School of Computer Science, University of Waterloo, Canada Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2008-12-01 11:15:09 Model selection Estimation theory Statistical theory Expectation–maximization algorithm Gamma distribution Variational Bayesian methods Bayesian information criterion Conjugate prior Kullback–Leibler divergence Statistics Bayesian statistics Bayesian inference
713	Automated Hierarchical Mixtures of Probabilistic Principal Component Analyzers Ting Su [removed] Jennifer G. Dy Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2008-12-01 11:20:35 Multivariate statistics Estimation theory Cluster analysis K-means clustering Mixture model Expectation–maximization algorithm Hierarchical clustering Bayesian information criterion Principal component analysis Statistics Machine learning Data analysis
714	Efficiently Learning Linear-Linear Exponential Family Predictive Representations of State David Wingate [removed] Satinder Singh Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2008-05-13 13:44:38 Markov models Control theory Kalman filter Robot control Maximum likelihood Logarithm Matrix Static single assignment form Expectation–maximization algorithm Statistics Mathematics Estimation theory
715	Local Likelihood Modeling of Temporal Text Streams Guy Lebanon Yang Zhao Department of Statistics, Purdue University – West Lafayette, IN 47907 Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2008-05-01 14:30:50 M-estimators Regression analysis Econometrics Maximum likelihood Nonparametric regression Likelihood principle Likelihood function Kernel smoother Expectation–maximization algorithm Statistics Estimation theory Non-parametric statistics
716	Bayesian Haplotype Inference via the Dirichlet Process Eric Xing† [removed] Roded Sharan† [removed] Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2008-12-01 11:20:48 Population genetics Genetic genealogy Haplotype Dirichlet process Mixture model Expectation–maximization algorithm Gibbs sampling Dirichlet distribution Hardy–Weinberg principle Statistics Genetics Classical genetics
717	1 Expectation-Maximization Algorithm and Image Segmentation Daozheng Chen In computer vision, image segmentation problem is to partition a digital image Add to Reading List Source URL: www.cs.umd.edu Language: English - Date: 2013-03-06 15:23:54 Bayesian statistics Multivariate statistics Cluster analysis Expectation–maximization algorithm Maximum likelihood K-means clustering Mixture model Segmentation Likelihood function Statistics Estimation theory Machine learning
718	On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution Masashi Sugiyama [removed] Makoto Yamada Add to Reading List Source URL: www.icml-2011.org Language: English - Date: 2011-06-01 14:49:37 Estimation theory Machine learning Signal processing Cluster analysis K-means clustering Expectation–maximization algorithm Spectral clustering Maximum likelihood Principal component analysis Statistics Multivariate statistics Data analysis
719	Variable Selection in Model-Based Clustering: To Do or To Facilitate Leonard K. M. Poon† [removed] Nevin L. Zhang† [removed] Add to Reading List Source URL: www.icml2010.org Language: English - Date: 2010-06-13 09:06:41 Multivariate statistics Cluster analysis Psychometrics Latent class model Expectation–maximization algorithm Latent variable Mixture model Mutual information Data analysis Statistics Statistical models Machine learning
720	Painless Unsupervised Learning with Features Taylor Berg-Kirkpatrick Alexandre Bouchard-Cˆot´e John DeNero Computer Science Division University of California at Berkeley Add to Reading List Source URL: www.cs.berkeley.edu Language: English - Date: 2010-08-26 16:46:00 Computational linguistics Markov models Corpus linguistics Speech recognition Machine learning Expectation–maximization algorithm Hidden Markov model N-gram Part-of-speech tagging Linguistics Statistics Science

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