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Mixture model
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#	Item
671	A Method of Moments for Mixture Models and Hidden Markov Models Anima Anandkumar@ Daniel Hsu# Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2014-11-25 10:45:21 Machine learning Mixture model Hidden Markov model Statistics Probability and statistics Cluster analysis
672	Learning Mixtures of Spherical Gaussians: Moment Methods and Spectral Decompositions Daniel Hsu and Sham M. Kakade Microsoft Research, New England Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2014-11-25 10:45:21 Computational statistics Mixture model Supervised learning Unsupervised learning Machine learning Statistics Artificial intelligence
673	Zucchini, Schmidt35(2) and vonresearch Gadow articles Silva Fennica Add to Reading List Source URL: www.silvafennica.fi Language: English - Date: 2013-05-07 06:57:55 Statistical theory Statistical models Probability distributions Multivariate normal distribution Normal distribution Errors and residuals in statistics Mixture model Joint probability distribution Mixture distribution Statistics Estimation theory Regression analysis
674	Identifiability and Unmixing of Latent Parse Trees Daniel Hsu Microsoft Research Sham M. Kakade Microsoft Research Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2014-11-25 10:37:13 Identifiability Mixture model Expectation–maximization algorithm Dimensional analysis Statistics Estimation theory Statistical models
675	February 20, 2014 Time: 10:25am fm.tex Add to Reading List Source URL: press.princeton.edu Language: English - Date: 2014-04-07 17:06:48 Econometrics Bayesian inference Robust statistics Non-parametric statistics Bayes factor Mixture model Bootstrapping Regression analysis Linear regression Statistics Statistical inference Bayesian statistics
676	Contrastive Learning Using Spectral Methods James Zou Harvard University Daniel Hsu Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2014-11-25 10:45:20 Probability and statistics Mixture model Latent Dirichlet allocation Hidden Markov model Topic model Tensor product Principal component analysis Statistics Algebra Statistical natural language processing
677	Learning Linear Bayesian Networks with Latent Variables Animashree Anandkumar Department of EECS, University of California, Irvine [removed] Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2014-11-25 10:45:20 Graphical models Bayesian statistics Networks Bayesian network Directed acyclic graph Mixture model Latent variable Expectation–maximization algorithm Independent component analysis Statistics Statistical models Estimation theory
678	Cognition[removed]–16 Contents lists available at ScienceDirect Cognition journal homepage: www.elsevier.com/locate/COGNIT Add to Reading List Source URL: ruccs.rutgers.edu Language: English - Date: 2014-06-26 14:22:55 Regularization Variance Economic model Mixture model Estimation theory F-test Overfitting Model Supervised learning Statistics Machine learning Concept learning
679	Unsupervised Domain Relevance Estimation for Word Sense Disambiguation Alfio Gliozzo and Bernardo Magnini and Carlo Strapparava ITC-irst, Istituto per la Ricerca Scientifica e Tecnologica, I[removed]Trento, ITALY {gliozzo, Add to Reading List Source URL: wndomains.fbk.eu Language: English - Date: 2006-12-18 06:13:58 Statistics Semantics Natural language processing Estimation theory Word-sense disambiguation WordNet SemEval Mixture model Expectation–maximization algorithm Science Linguistics Computational linguistics
680	A Spectral Algorithm for Latent Dirichlet Allocation Anima Anandkumar University of California Irvine, CA [removed] Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2014-11-25 10:45:21 Statistical models Bayesian networks Latent Dirichlet allocation Natural language processing Expectation–maximization algorithm Hidden Markov model Mixture model Dirichlet distribution Topic model Statistics Machine learning Statistical natural language processing

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