Random field - PDFSEARCH.IO - Document Search Engine

Random field
Results: 650

#	Item
201	TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation J. Shotton2 , J. Winn1 , C. Rother1 , and A. Criminisi1 1 Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2006-02-14 13:30:00 Statistics Vision Segmentation Conditional random field Shape context Constellation model Maximum likelihood Object recognition Statistical classification Machine learning Computer vision Artificial intelligence
202	Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement Jin Tian Department of Computer Science Iowa State University Add to Reading List Source URL: www.cs.iastate.edu Language: English - Date: 2005-06-01 16:14:08 Bayesian statistics Statistical models Networks Ancestral graph Bayesian network M-separation Graph Directed graph Markov random field Graph theory Graphical models Statistics
203	Cross-lingual Projected Expectation Regularization for Weakly Supervised Learning Mengqiu Wang and Christopher D. Manning Computer Science Department Stanford University Stanford, CAUSA Add to Reading List Source URL: cs.stanford.edu Language: English - Date: 2014-03-05 02:07:11 Computational linguistics Econometrics Bitext word alignment Machine translation Estimation theory Conditional random field Semi-supervised learning Named-entity recognition Expectation–maximization algorithm Statistics Machine learning Science
204	The method used to calculate the ionospheric electron density is based on statistical inversion theory. Both the measurements and the unknown electron densities are treated as random variables, and the algorithm gives th Add to Reading List Source URL: sgodata.sgo.fi Language: English - Date: 2003-11-25 10:52:22 Summability methods Regularization Quantum chemistry Physics Quantum field theory Electron
205	Webpage Understanding: an Integrated Approach Jun Zhu ∗ Dept. of Comp. Sci. & Tech. Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2007-06-15 03:38:48 Mathematics Statistics Machine learning Artificial intelligence Probability theory Conditional random field Hidden Markov model Pattern recognition Markov random field Theoretical computer science Graphical models Applied mathematics
206	Effect of Non-linear Deep Architecture in Sequence Labeling Mengqiu Wang MENGQIU @ CS . STANFORD . EDU Christopher D. Manning MANNING @ CS . STANFORD . EDU Add to Reading List Source URL: cs.stanford.edu Language: English - Date: 2013-05-15 05:53:10 Mathematics Science Computational neuroscience Conditional random field Theoretical computer science Perceptron Chunking Artificial neuron Supervised learning Neural networks Machine learning Statistics
207	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: www.psi.toronto.edu Language: English - Date: 2005-01-17 09:27:09 Factor graph Belief propagation Tree decomposition Directed acyclic graph Bayesian network Markov random field Graph coloring Path decomposition Graph theory Mathematics Graphical models
208	Hidden Conditional Random Fields for Phone Recognition Yun-Hsuan Sung 1 and Dan Jurafsky 2 1 Electrical Engineering, Stanford University Add to Reading List Source URL: nlp.stanford.edu Language: English - Date: 2010-01-07 17:30:55 Probability and statistics Probability Hidden Markov model Conditional random field Speech recognition Mutual information Sequence labeling Generative model Viterbi algorithm Statistics Machine learning Markov models
209	FROM FLAT DIRECT MODELS TO SEGMENTAL CRF MODELS Geoffrey Zweig and Patrick Nguyen Microsoft Corporation One Microsoft Way, Redmond, WAABSTRACT This paper summarizes recent work at Microsoft on the development of n Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2010-01-06 19:40:40 Computational linguistics Conditional random field Maximum-entropy Markov model Speech recognition N-gram Language model Dynamic programming Part-of-speech tagging Information extraction Markov models Science Probability
210	MAXIMUM CONDITIONAL LIKELIHOOD LINEAR REGRESSION AND MAXIMUM A POSTERIORI FOR HIDDEN CONDITIONAL RANDOM FIELDS SPEAKER ADAPTATION Yun-Hsuan Sung,1 Constantinos Boulis,2 Dan Jurafsky3 Electrical Engineering,1 Linguistics2 Add to Reading List Source URL: nlp.stanford.edu Language: English - Date: 2008-04-28 14:26:34 Econometrics Machine learning Markov models Hidden Markov model Maximum likelihood Linear regression Pattern recognition Normal distribution Conditional random field Statistics Estimation theory Regression analysis

UPDATE