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Expectation–maximization algorithm
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91	Hierarchical Mixture Models for Nested Data Structures Jeroen K. Vermunt1 and Jay Magidson2 1 2 Add to Reading List Source URL: www.statisticalinnovations.com Language: English - Date: 2006-09-05 12:15:13 Regression analysis Categorical data Estimation theory Expectation–maximization algorithm Mixture model Latent class model Mixture distribution Hidden Markov model Markov chain Statistics Statistical models Markov models
92	A Distance Model for Rhythms Jean-Fran¸ cois Paiement Yves Grandvalet IDIAP Research Institute, Case Postale 592, CH-1920 Martigny, Switzerland Add to Reading List Source URL: bengio.abracadoudou.com Language: English - Date: 2008-06-27 10:33:55 Dynamic programming Bioinformatics Estimation theory Hidden Markov model Machine learning Levenshtein distance Expectation–maximization algorithm Mixture model Statistics Probability and statistics Markov models
93	Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models Add to Reading List Source URL: www.personal.psu.edu Language: English - Date: 2008-07-18 23:29:17 Statistical theory Econometrics Decision theory Elastic net regularization Least squares Tikhonov regularization Least-angle regression Expectation–maximization algorithm Lasso Statistics Estimation theory Regression analysis
94	FACTORIZED CONTEXT MODELLING FOR TEXT-TO-SPEECH SYNTHESIS Heng Lu, Simon King The Centre for Speech Technology Research, The University of Edinburgh, UK , ABSTRACT Add to Reading List Source URL: www.cstr.ed.ac.uk Language: English - Date: 2013-04-25 21:11:16 Bayesian statistics Markov models Computational linguistics Graphical models Hidden Markov model Bayesian network Speech recognition Mixture model Expectation–maximization algorithm Statistics Statistical models Probability and statistics
95	A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data: Supplementary Material Do-kyum Kim, Matthew Der and Lawrence K. Saul Department of Computer Science and Engineering, Univers Add to Reading List Source URL: cseweb.ucsd.edu Language: English - Date: 2015-02-25 18:37:19 Data analysis Probability theory Expectation–maximization algorithm Variance Statistics Estimation theory Central limit theorem
96	Similarity-Based Alignment and Generalization Daniel Oblinger, Vittorio Castelli, Tessa Lau, and Lawrence D. Bergman IBM T.J. Watson Research, New York oblio,vittorio,tessalau, Abstract. We present a Add to Reading List Source URL: tlau.org Language: English - Date: 2005-11-29 11:04:24 Machine learning Computational statistics Computational phylogenetics Hidden Markov model Sequence alignment Algorithm Baum–Welch algorithm Markov chain Expectation–maximization algorithm Markov models Statistics Bioinformatics
97	Latent Class Analysis Jeroen K. Vermunt & Jay Magidson The basic idea underlying latent class (LC) analysis is a very simple one: some of the parameters of a postulated statistical model differ across unobserved subgroup Add to Reading List Source URL: www.statisticalinnovations.com Language: English - Date: 2006-09-05 12:11:48 Econometrics Categorical data Multivariate statistics Latent class model Expectation–maximization algorithm Latent variable model Structural equation modeling Logistic regression Mixture model Statistics Regression analysis Statistical models
98	Multi-View Clustering via Canonical Correlation Analysis Kamalika Chaudhuri ITA, UC San Diego, 9500 Gilman Drive, La Jolla, CA Add to Reading List Source URL: cseweb.ucsd.edu Language: English - Date: 2011-01-01 02:41:48 Machine learning Cluster analysis Data mining K-means clustering Principal component analysis Hierarchical clustering Expectation–maximization algorithm Mixture model Singular value decomposition Statistics Multivariate statistics Data analysis
99	Unsupervised Learning∗ Zoubin Ghahramani† Gatsby Computational Neuroscience Unit University College London, UK http://www.gatsby.ucl.ac.uk/~zoubin Add to Reading List Source URL: www.inf.ed.ac.uk Language: English - Date: 2005-11-29 12:55:06 Statistical theory Machine learning Markov models Expectation–maximization algorithm Maximum likelihood Hidden Markov model Mixture model Kullback–Leibler divergence Marginal likelihood Statistics Estimation theory Bayesian statistics
100	A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data Do-kyum Kim, Matthew Der and Lawrence K. Saul Department of Computer Science and Engineering, University of California, San D Add to Reading List Source URL: cseweb.ucsd.edu Language: English - Date: 2014-02-24 20:30:41 Estimation theory Learning Statistical theory Semi-supervised learning Expectation–maximization algorithm Supervised learning Support vector machine Maximum likelihood Stability Statistics Machine learning Statistical classification

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