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Artificial selection
Results: 368

#	Item
261	Integrating Randomization and Discrimination for Classifying Human-Object Interaction Activities Aditya Khosla, Bangpeng Yao and Li Fei-Fei 1 Introduction Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2014-02-01 15:26:45 Statistical classification Decision trees Ensemble learning Computer vision Random forest Feature selection Object recognition Decision tree learning Segmentation Statistics Machine learning Artificial intelligence
262	Canonical Image Selection from the Web Yushi Jing1,2 [removed] Shumeet Baluja2 [removed] Add to Reading List Source URL: www.kevinjing.com Language: English - Date: 2012-11-29 21:42:37 Vision Computer vision Artificial intelligence Information retrieval Content-based image retrieval Image retrieval Google Images Object recognition Yahoo! Information science Image search Internet search engines
263	CAPIR: Collaborative Action Planning with Intention Recognition Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2012-06-11 20:17:25 Stochastic control Markov models Artificial intelligence Action selection Markov decision process Ghostbusters Markov chain Reinforcement learning Statistics Markov processes Dynamic programming
264	D By Elishia Ballentine, Editor id you know there were “Blue Pyramids” in Alabama? That’s the name given to one selection of a variety of Christmas trees grown at the Add to Reading List Source URL: www.forestry.alabama.gov Language: English - Date: 2012-02-14 09:15:35 Christmas traditions Christmas tree Christmas Christmas tree farming Artificial Christmas trees
265	Dynamic Analysis of Multiagent Q-learning with Exploration ǫ-greedy Eduardo Rodrigues Gomes Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2009-05-18 12:17:09 Multi-agent systems Reinforcement learning Q-learning Agent-based model Action selection Affect Machine learning Intelligent agent Artificial intelligence Science Mind
266	Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP-02), pp[removed]Philadelphia, PA, July, 2002 Combining Sample Selection and Error-Driven Pruning for Machine Learning of Coref Add to Reading List Source URL: www.cs.cornell.edu Language: English - Date: 2004-07-16 12:17:06 Natural language processing Computational linguistics Artificial intelligence Information extraction Coreference Statistical classification Message Understanding Conference Pruning Cluster analysis Statistics Complexity classes Machine learning
267	Optimizing to Arbitrary NLP Metrics using Ensemble Selection Art Munson, Claire Cardie, Rich Caruana Department of Computer Science Cornell University Ithaca, NY 14850 {mmunson, cardie, caruana}@cs.cornell.edu Add to Reading List Source URL: www.cs.cornell.edu Language: English - Date: 2006-02-09 13:57:17 Statistics Science Natural language processing Cybernetics Theoretical computer science Ensemble learning Supervised learning Support vector machine Statistical classification Machine learning Computational linguistics Artificial intelligence
268	Minimum Reference Set Based Feature Selection for Small Sample Classifications Xue-wen Chen [removed] Jong Cheol Jeong Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2008-12-01 11:26:27 Statistical classification Support vector machine K-nearest neighbor algorithm VC dimension Feature selection Structural risk minimization Training set Least squares support vector machine Supervised learning Statistics Machine learning Artificial intelligence
269	Figure S 1. Comparison of KNN models submitted to MAQC-II project. MCC performance comparison of all KNN models among four teams. All teams generally agree that endpoints E, H, and L are easy and result in high performa Add to Reading List Source URL: www.nature.com Language: English - Date: 2010-07-30 10:26:07 Artificial intelligence Learning Cross-validation K-nearest neighbor algorithm Statistical classification Training set Data analysis Feature selection Statistics Machine learning Model selection
270	An Introduction to the Good, the Bad, & the Ugly Face Recognition Challenge Problem P. Jonathon Phillips, J. Ross Beveridge, Bruce A. Draper, Geof Givens, Alice J. O’Toole, David S. Bolme, Joseph Dunlop, Yui Man Lui, H Add to Reading List Source URL: www.nist.gov Language: English - Date: 2013-09-19 11:36:20 Security Mathematics Face Recognition Grand Challenge FERET Multiple Biometric Grand Challenge Face Recognition Vendor Test Facial recognition system Selection algorithm Partition Face recognition Biometrics Artificial intelligence

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