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Andrew McCallum
Results: 67

#	Item
51	3 Semi-Supervised Text Classification Using EM Kamal Nigam Andrew McCallum Tom Mitchell Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2006-03-07 20:47:10 Supervised learning Naive Bayes classifier Co-training Mixture model Document classification Generative model Expectation–maximization algorithm Statistical classification Maximum likelihood Statistics Machine learning Semi-supervised learning
52	Dynamic Sharing and Backward Compatibility on 64-Bit Machines William E. Garrett, Ricardo Bianchini, Leonidas Kontothanassis, R. Andrew McCallum, Jeffery Thomas, Robert Wisniewski, and Michael L. Scott Add to Reading List Source URL: www.cs.rochester.edu Language: English - Date: 2011-03-27 15:57:11 Virtual memory Library Position-independent code Kernel Procfs Relocation Virtual address space Portable Executable Fork System software Computing Computer architecture
53	Structured Relation Discovery using Generative Models Limin Yao∗ Aria Haghighi+ Sebastian Riedel∗ Andrew McCallum∗ ∗ Add to Reading List Source URL: people.cs.umass.edu Language: English - Date: 2011-08-01 22:02:37 Relational model Finitary relation Tuple Relational database Binary relation Entity-relationship model Information extraction Relation Extension Mathematics Mathematical logic Logic
54	Unsupervised Relation Discovery with Sense Disambiguation Limin Yao Sebastian Riedel Andrew McCallum Department of Computer Science Add to Reading List Source URL: people.cs.umass.edu Language: English - Date: 2012-07-26 18:14:09 Semantics Lexical semantics Natural language processing Logic Statistical natural language processing Word-sense disambiguation Hierarchical clustering Finitary relation Sense and reference Linguistics Science Computational linguistics
55	Cryptogram Decoding for Optical Character Recognition Gary Huang, Erik Learned-Miller, Andrew McCallum Department of Computer Science University of Massachusetts Amherst, MA 01003 {ghuang, elm, [removed]} Add to Reading List Source URL: vis-www.cs.umass.edu Language: English - Date: 2008-01-02 15:36:21 Artificial intelligence Multivariate statistics Cluster analysis Data analysis Data mining Geostatistics N-gram Code Segmentation Statistics Science Computational linguistics
56	Cryptogram Decoding for Optical Character Recognition Gary Huang, Erik Learned-Miller, Andrew McCallum University of Massachusetts Department of Computer Science 140 Governors Drive, Amherst, MA 01003 {ghuang, elm, mccal Add to Reading List Source URL: vis-www.cs.umass.edu Language: English - Date: 2008-01-02 15:36:21 Artificial intelligence N-gram Natural language processing Speech recognition Optical character recognition Code Computational linguistics Science Linguistics
57	An Exploration of Entity Models, Collective Classification and Relation Description Hema Raghavan, James Allan and Andrew McCallum Center for Intelligent Information Retrieval Department of Computer Science University of Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2004-08-04 04:00:13 Computational linguistics Speech recognition Statistical classification Information retrieval Entity-relationship model Information extraction Data model Naive Bayes classifier Text Retrieval Conference Natural language processing Science Statistics
58	Polylingual Topic Models David Mimno Hanna M. Wallach Jason Naradowsky David A. Smith Andrew McCallum University of Massachusetts, Amherst Amherst, MA 01003 {mimno, wallach, narad, dasmith, mccallum}@cs.umass.edu Add to Reading List Source URL: www.ccs.neu.edu Language: English - Date: 2009-08-10 15:55:47 Science Latent Dirichlet allocation Tuple Statistical machine translation Topic model Language model Text corpus Information retrieval Machine translation Statistical natural language processing Linguistics Natural language processing
59	Inference by Minimizing Size, Divergence, or their Sum Sebastian Riedel David A. Smith Andrew McCallum Department of Computer Science Add to Reading List Source URL: www.ccs.neu.edu Language: English - Date: 2010-11-08 21:30:30 Mathematics Probability theory Statistical theory Kullback–Leibler divergence Thermodynamics Belief propagation Markov random field Factor graph Divergence Graph theory Statistics Graphical models
60	Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data Charles Sutton Khashayar Rohanimanesh Andrew McCallum Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2008-12-01 11:19:43 Probability and statistics Probability Machine learning Bayesian statistics Statistical models Conditional random field Sequence labeling Hidden Markov model Part-of-speech tagging Statistics Markov models Graphical models

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