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Expectation
Results: 2405

#	Item
961	High-Frequency Shape and Albedo from Shading using Natural Image Statistics Jonathan T. Barron and Jitendra Malik University of California, Berkeley Berkeley, CA, 94720 {barron, malik}@eecs.berkeley.edu Add to Reading List Source URL: www.cs.berkeley.edu Language: English - Date: 2012-07-28 19:03:23 Edge detection Image denoising Expectation–maximization algorithm Shading Rendering Statistics Image processing Computer vision
962	Training deformable part models with decorrelated features Ross Girshick and Jitendra Malik UC Berkeley {rbg, malik}@eecs.berkeley.edu Abstract Add to Reading List Source URL: www.cs.berkeley.edu Language: English - Date: 2013-10-14 17:32:54 Multivariate statistics Linear discriminant analysis Histogram of oriented gradients Support vector machine Expectation–maximization algorithm Filter Estimation theory Statistics Statistical classification Signal processing
963	Shared Logistic Normal Distributions for Soft Parameter Tying in Unsupervised Grammar Induction Shay B. Cohen and Noah A. Smith Language Technologies Institute School of Computer Science Carnegie Mellon University Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2009-03-31 16:28:07 Estimation theory Bayesian statistics Statistical theory Bioinformatics Expectation–maximization algorithm Variational Bayesian methods Gamma distribution Multinomial distribution Dirichlet distribution Statistics Probability and statistics Statistical natural language processing
964	Composite Statistical Inference for Semantic Segmentation Fuxin Li(1) , Joao Carreira(2) , Guy Lebanon(1) , Cristian Sminchisescu[removed]Georgia Institute of Technology. (2) ISR - University of Coimbra. (3) Lund Universi Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2014-06-04 23:25:14 Maximum likelihood Expectation–maximization algorithm Identifiability Score Likelihood function Parametric model M-estimator Mixture model Entailment Statistics Estimation theory Segmentation
965	Microsoft Word - EQM without branching time for web.docx Add to Reading List Source URL: alastairwilson.org Language: English - Date: 2012-05-10 11:53:14 Interpretations of quantum mechanics Many-worlds interpretation Quantum decoherence Consistent histories Measurement problem Measurement in quantum mechanics Density matrix Expectation value Physics Quantum measurement Quantum mechanics
966	Mixture models for describing some strange looking datasets Add to Reading List Source URL: www.rsc.org Language: English - Date: 2014-08-20 04:20:15 Statistical models Statistical theory Expectation–maximization algorithm Missing data Mixture model Maximum likelihood Least squares Normal distribution Mixture distribution Statistics Estimation theory Regression analysis
967	CS 294-2, Grouping and Recognition (Prof. Jitendra Malik) Aug 30, 1999 Lecture #3 (Maximum likelihood framework) DRAFT Notes by Joshua Levy Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2003-07-25 14:52:15 Normal distribution Expectation–maximization algorithm Estimator Likelihood function M-estimator Estimation of covariance matrices Statistics Estimation theory Maximum likelihood
968	CS 294-2, Grouping and Recognition (Prof. Jitendra Malik) Sept 1st, 1999 Lecture #4 (Expectation Maximization Algorithm) DRAFT Notes by Rene Vidal Mixture models with incomplete data Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2003-07-25 14:54:43 Statistical theory Expectation–maximization algorithm Missing data Normal distribution Mixture model Maximum likelihood Multivariate normal distribution Statistics Estimation theory Cluster analysis
969	An Empirical Comparison of Techniques for Handling Incomplete Data Using Decision Trees BHEKISIPHO TWALA Modelling and Digital Intelligence, CSIR, P O Box 395, Pretoria 0001, South Africa ________________________________ Add to Reading List Source URL: researchspace.csir.co.za Language: English - Date: 2011-01-28 14:41:17 Machine learning Decision trees Imputation Data mining Decision tree learning Data set Expectation–maximization algorithm Supervised learning Censoring Statistics Data analysis Missing data
970	Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability Ramesh Nallapati, William Cohen and John Lafferty Machine Learning Department Carnegie Mellon University 50 Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2007-09-24 14:58:31 Missing data Latent Dirichlet allocation Linear discriminant analysis Statistics Estimation theory Expectation–maximization algorithm