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Random field
Results: 650

#	Item
311	Collective Activity Detection using Hinge-loss Markov Random Fields Ben London, Sameh Khamis, Stephen H. Bach, Bert Huang, Lise Getoor, Larry Davis University of Maryland College Park, MD 20742 {blondon,sameh,bach,bert,g Add to Reading List Source URL: cvpr13ws.is.tue.mpg.de Language: English - Date: 2013-06-26 12:59:39 Artificial intelligence Statistics Bayesian statistics Graphical models Markov models Markov random field Hidden Markov model Activity recognition Causality Science Probability Theoretical computer science
312	Who Killed the Directed Model? Justin Domke, Alap Karapurkar, and Yiannis Aloimonos Department of Computer Science University of Maryland {domke,karapurk,yiannis}@cs.umd.edu Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2011-11-07 14:37:12 Bayesian statistics Statistical models Estimation theory Graphical models Markov random field Mixture model Maximum likelihood Kullback–Leibler divergence Hidden Markov model Statistics Probability and statistics Statistical theory
313	Journal of Machine Learning Research[removed]2173 Submitted 2/10; Revised 8/10; Published 8/10 libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models Add to Reading List Source URL: www.jmlr.org Language: English - Date: 2010-08-23 18:11:12 Mathematics Belief propagation Factor graph Bayesian network Markov random field Conditional random field Gibbs sampling Expectation–maximization algorithm Tree decomposition Graphical models Graph theory Statistics
314	Object Recognition by Combining Appearance and Geometry David Crandall1 , Pedro Felzenszwalb2 , and Daniel Huttenlocher1 1 Cornell University Add to Reading List Source URL: vision.soic.indiana.edu Language: English - Date: 2014-08-03 00:38:07 Object recognition Markov random field Applied mathematics Computer vision Probability Artificial intelligence
315	498 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 47, NO. 2, FEBRUARY 2001 Factor Graphs and the Sum-Product Algorithm Frank R. Kschischang, Senior Member, IEEE, Brendan J. Frey, Member, IEEE, and Add to Reading List Source URL: cba.mit.edu Language: English - Date: 2011-12-13 18:50:13 Factor graph Belief propagation Tree decomposition Directed acyclic graph Bayesian network Markov random field Graph coloring Path decomposition Graph theory Mathematics Graphical models
316	A collection F of subsets of Ω is a field (i) if A, B ∈ F then A ∪ B ∈ F and A ∩ B ∈ F, (ii) if A ∈ F then Ac ∈ F, (iii) ∅ ∈ F. c σ-field: (i) ∅ ∈ F, (ii) if A1 , A2 , . . . ∈ F then ∪∞ i=1 Add to Reading List Source URL: eigenmath.sourceforge.net Language: English - Date: 2014-12-07 12:48:24 Probability theory Information theory Limit superior and limit inferior Continuous function Multivariate random variable Conditioning Itō diffusion Mathematical analysis Mathematics Statistics
317	l5-variable-elimination.dvi Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2015-02-03 13:09:02 Mathematical analysis Probability theory Compiler construction Static single assignment form Function Markov random field Bayesian network Μ operator Golden ratio base Mathematics Graphical models Networks
318	Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition David J. Crandall and Daniel P. Huttenlocher Cornell University, Ithaca, NY 14850, USA, {crandall,dph}@cs.cornell.edu Add to Reading List Source URL: vision.soic.indiana.edu Language: English - Date: 2014-08-03 00:38:09 Machine learning Artificial intelligence Maximum likelihood Expectation–maximization algorithm Object recognition Supervised learning Markov random field Constellation model One-shot learning Statistics Estimation theory Statistical theory
319	Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition David J. Crandall and Daniel P. Huttenlocher Cornell University, Ithaca, NY 14850, USA, {crandall,dph}@cs.cornell.edu Add to Reading List Source URL: www.cs.cornell.edu Language: English - Date: 2006-02-18 10:40:24 Machine learning Artificial intelligence Maximum likelihood Expectation–maximization algorithm Object recognition Supervised learning Markov random field Constellation model One-shot learning Statistics Estimation theory Statistical theory
320	Conditional Random Fields for Multi-agent Reinforcement Learning Xinhua Zhang [removed] Douglas Aberdeen [removed] Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2009-06-21 14:46:49 Estimation theory Machine learning Conditional random field Theoretical computer science Reinforcement learning Loss function Markov decision process Rao–Blackwell theorem Bayesian network Statistics Graphical models Statistical theory

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