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Carnegie Learning
Results: 541

#	Item
111	A Learning Theory Approach to Noninteractive Database Privacy AVRIM BLUM, Carnegie Mellon University KATRINA LIGETT, California Institute of Technology AARON ROTH, University of Pennsylvania In this article, we demonstr Add to Reading List Source URL: users.cms.caltech.edu Language: English - Date: 2013-05-29 13:54:34
112	Training fMRI Classifiers to Detect Cognitive States across Multiple Human Subjects Xuerui Wang, Rebecca Hutchinson, and Tom M. Mitchell Center for Automated Learning and Discovery Carnegie Mellon University Add to Reading List Source URL: web.engr.oregonstate.edu Language: English - Date: 2005-02-05 01:47:12
113	Multi-view Exploratory Learning for AKBC Problems William W. Cohen School of Computer Science Carnegie Mellon University Add to Reading List Source URL: www.akbc.ws Language: English - Date: 2015-04-02 16:06:46 Statistics Semi-supervised learning Document classification Expectation–maximization algorithm Supervised learning K-means clustering Word-sense disambiguation Concept learning Statistical classification Machine learning Computational statistics Artificial intelligence
114	Approximate Parameter Learning in Discriminative Fields Sanjiv Kumar and Martial Hebert The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 Add to Reading List Source URL: www.sanjivk.com Language: English - Date: 2010-06-01 18:50:54 M-estimators Econometrics Bayesian statistics Maximum likelihood Likelihood function Loss function Statistics Estimation theory Statistical theory
115	Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification Sanjiv Kumar and Martial Hebert The Robotics Institute, Carnegie Mellon University Pittsburgh, PA 15213, USA, {skumar, Add to Reading List Source URL: www.sanjivk.com Language: English - Date: 2010-06-01 18:49:35 Theoretical computer science Probability Graphical models Statistical models Bayesian statistics Conditional random field Discriminative model Generative model Markov random field Statistics Machine learning Probability and statistics
116	A Hierarchical Field Framework for Unified Context-Based Classification Sanjiv Kumar and Martial Hebert The Robotics Institute, Carnegie Mellon University Pittsburgh, PA 15213, USA, {skumar, hebert}@ri.cmu.edu Abstract Add to Reading List Source URL: www.sanjivk.com Language: English - Date: 2010-06-01 18:50:20 Vision Imaging Bayesian statistics Object recognition Segmentation Maximum likelihood Finitary relation Bayesian network One-shot learning Computer vision Image processing Statistics
117	Multiclass Discriminative Fields for Parts-Based Object Detection Sanjiv Kumar and Martial Hebert The Robotics Institute Carnegie Mellon University Add to Reading List Source URL: www.sanjivk.com Language: English - Date: 2010-06-01 18:50:50 Imaging Object recognition Initial and terminal objects Conditional random field One-shot learning Computer vision Vision Artificial intelligence
118	Tree-Based Inference for Dirichlet Process Mixtures Yang Xu Machine Learning Department School of Computer Science Carnegie Mellon University Add to Reading List Source URL: www.gatsby.ucl.ac.uk Language: English - Date: 2009-04-21 20:35:55
119	Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study Sanjiv Kumar, Jonas August, and Martial Hebert The Robotics Institute, Carnegie Mellon University Pittsburgh, PA 15213 Add to Reading List Source URL: www.sanjivk.com Language: English - Date: 2010-06-01 18:50:53 Graphical models Bayesian statistics Probability theory Maximum likelihood Likelihood function Conditional random field Belief propagation Statistics Estimation theory M-estimators
120	Intelligent Workstation Agents and Unstructured Workstation Data Tom M. Mitchell Center for Automated Learning and Discovery Carnegie Mellon University September, 2004 Add to Reading List Source URL: www.wi-consortium.org Language: English - Date: 2004-12-01 01:50:07

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