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Feature learning
Results: 920

#	Item
441	NIPS03 Workshop of Feature Extraction and Feature Selection Challenge Feature Selection using/for Transductive Support Vector Machine Mr. Zhili Wu Dr. Chun-hung Li Add to Reading List Source URL: clopinet.com Language: English - Date: 2003-12-18 12:54:20 Transduction Model selection Support vector machine Feature selection Naive Bayes classifier Statistics Statistical classification Machine learning
442	Visual Methods for Examining SVM Classiﬁers Doina Caragea1, Dianne Cook2 , Hadley Wickham2 , and Vasant Honavar3 1 3 Add to Reading List Source URL: www.cs.iastate.edu Language: English - Date: 2009-02-02 00:36:38 Model selection Learning Support vector machines Cross-validation Feature selection Linear discriminant analysis Stability Statistics Machine learning Statistical classification
443	Nameless Feature Selection Challenge Attempt By Ran Gilad-Bachrach and Amir Navot Overview Add to Reading List Source URL: clopinet.com Language: English - Date: 2003-12-10 05:55:54 Statistical classification Support vector machine Feature selection K-nearest neighbor algorithm Algorithm Learning Vector Quantization Transduction Selection Naive Bayes classifier Statistics Machine learning Artificial intelligence
444	A study of the NIPS feature selection challenge Nicholas Johnson November 29, 2009 Abstract The 2003 Nips Feature extraction challenge was dominated by Bayesian approaches developed by the team of Radford Neal and Jiangu Add to Reading List Source URL: statweb.stanford.edu Language: English - Date: 2009-12-14 18:18:01 Ensemble learning Neural networks Regression analysis Computational neuroscience Early stopping Boosting Overfitting Statistical classification Logistic regression Machine learning Statistics Learning
445	AIJ special issue on relevance Wrappers for Feature Subset Selection Ron Kohavi Data Mining and Visualization Silicon Graphics, Inc. Add to Reading List Source URL: ai.stanford.edu Language: English - Date: 2010-10-25 23:04:29 Knowledge Dimension reduction Feature selection Model selection Supervised learning Pattern recognition Algorithm NP Relevance Machine learning Science Statistics
446	Biometric Systems: A Face Recognition Approach Erik Hjelmås∗ Department of Informatics University of Oslo P. O. Box 1080 Blindern, 0316 Oslo, Norway email: [removed] Add to Reading List Source URL: www.nik.no Language: English - Date: 2004-03-09 04:39:08 Face recognition Image processing Surveillance Machine learning Facial recognition system Feature Eigenface Face detection Pattern recognition Computer vision Artificial intelligence Vision
447	feature 074 EDUCATION TECHNOLOGY SOLUTIONS[removed]ETS_64 SpecialFeature.indd[removed]:47 pm Add to Reading List Source URL: www.yooyahcloud.com Language: English - Date: 2015-03-10 00:21:15 E-learning Mental health Educational psychology Education Pedagogy Blended learning
448	Sparse Feature Learning for Deep Belief Networks Marc’Aurelio Ranzato1 Y-Lan Boureau2,1 Yann LeCun1 1 Add to Reading List Source URL: yann.lecun.com Language: English - Date: 2007-11-14 18:21:11 Pulse-code modulation Quantization Overcompleteness Supervised learning Electronic engineering Mathematics Statistics Decoder Linear classifier
449	FRANCE-MEDIA-INTERNET-FEATURE Add to Reading List Source URL: www.cjcp.org.za Language: English - Date: 2014-06-17 05:26:56 Virtual reality Technology Internet privacy Social networking service Virtual world E-learning Video game Online identity Adolescence Human development Education
450	Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines Kihyuk Sohn Guanyu Zhou Chansoo Lee Honglak Lee Add to Reading List Source URL: jmlr.org Language: English - Date: 2013-08-14 01:36:44 Boltzmann machine Supervised learning Feature selection Pattern recognition Generative model Neural network Unsupervised learning Support vector machine Linear classifier Statistics Machine learning Artificial intelligence

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