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Random field
Results: 650

#	Item
361	Log-Linear Approach to Discriminative Training NIPS 2008 Workshop “Speech and Language: Learning-based Methods and Systems” Ralf Schl¨ uter, Georg Heigold, and Hermann Ney Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2009-01-03 02:48:23 Discriminative model Generative model Expectation–maximization algorithm Pattern recognition Conditional random field Speech recognition Log-linear model Hidden Markov model Maximum likelihood Statistics Machine learning Probability and statistics
362	Cue Integration for Figure/Ground Labeling Xiaofeng Ren, Charless C. Fowlkes and Jitendra Malik Computer Science Division, University of California, Berkeley, CA 94720 {xren,fowlkes,malik}@cs.berkeley.edu Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2006-01-31 22:59:56 Computer vision Vector calculus Estimation theory Statistics Segmentation Applied mathematics Normal Maximum likelihood Markov random field Image processing Mathematics Surfaces
363	Quadratic Programming Relaxations for Metric Labeling and Markov Random Field MAP Estimation Pradeep Ravikumar John Lafferty School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2006-05-19 14:30:14 Linear programming Convex optimization Linear programming relaxation Relaxation Quadratic programming Randomized rounding Duality Dynamic programming Mathematical optimization Operations research Mathematics
364	Conditional Random Fields via Univariate Exponential Families Eunho Yang Department of Computer Science University of Texas at Austin Add to Reading List Source URL: www.cs.utexas.edu Language: English - Date: 2013-11-14 22:48:54 Probability theory Graphical models Regression analysis Theoretical computer science Exponentials Conditional random field Generalized linear model Sufficient statistic Normal distribution Statistics Probability and statistics Probability
365	On Poisson Graphical Models Eunho Yang Department of Computer Science University of Texas at Austin [removed] Add to Reading List Source URL: www.cs.utexas.edu Language: English - Date: 2013-11-07 15:24:26 Categorical data Regression analysis Poisson processes Poisson distribution Maximum likelihood Expectation–maximization algorithm Markov random field Normal distribution Count data Statistics Estimation theory Actuarial science
366	Image Segmentation with Topic Random Field Bin Zhao1 , Li Fei-Fei2 , and Eric P. Xing1 1 School of Computer Science, Carnegie Mellon University 2 Add to Reading List Source URL: vision.stanford.edu Language: English - Date: 2013-09-22 18:29:41 Artificial intelligence Machine learning Statistical natural language processing Segmentation Latent Dirichlet allocation Expectation–maximization algorithm Object recognition Bag of words model in computer vision Statistics Image processing Computer vision
367	The Logistic Random Field – A Convenient Graphical Model for Learning Parameters for MRF-based Labeling Marshall F. Tappen, Kegan G. G. Samuel, Craig V. Dean, David M. Lyle University of Central Florida School of Elect Add to Reading List Source URL: www.eecs.ucf.edu Language: English - Date: 2008-06-17 17:00:32 Graphical models Theoretical computer science Image processing Segmentation Mixture model Conditional random field Loss function Linear regression Regression analysis Statistics Econometrics Machine learning
368	Incorporating Context and User Feedback in Pen-Based Interfaces Martin Szummer and Philip J. Cowans Microsoft Research Add to Reading List Source URL: www.inference.phy.cam.ac.uk Language: English - Date: 2004-11-05 05:01:58 Science Pattern recognition Conditional random field Labeling theory Segmentation Learning Bayesian network User interface Machine learning Graphical models Statistics
369	Mixed Graphical Models via Exponential Families Eunho Yang∗ ∗ Yulia Baker† Add to Reading List Source URL: jmlr.org Language: English - Date: 2014-04-14 20:16:23 Graphical models Theoretical computer science Exponentials Statistical models Markov random field Normal distribution Exponential family Gibbs sampling Conditional random field Statistics Probability and statistics Statistical theory
370	On Learning Discrete Graphical Models using Group-Sparse Regularization Ali Jalali ECE, UT Austin [removed] Add to Reading List Source URL: www.cs.utexas.edu Language: English - Date: 2011-04-08 11:45:11 Statistical models Graphical models Statistical theory Maximum likelihood Markov random field Logistic regression Fisher information Likelihood function Graphical models for protein structure Statistics Estimation theory Bayesian statistics

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