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Feature learning
Results: 920

#	Item
531	International Journal of Computer Applications (0975 – 8887) Volume 77– No., september 2013 Classification of the Lung Diseases from CT Scans by Advanced Segmentation Techniques using Genetic Algorithm Add to Reading List Source URL: research.ijcaonline.org Language: English* - Date: 2013-09-25 11:01:14 Computer vision Image processing Bayesian statistics Segmentation Naive Bayes classifier Feature selection Pattern recognition Random forest Statistical classification Statistics Machine learning Dimension reduction
532	Saturday, December 7, 2002 techLEARNING.com \| Technology & Learning - The Resource for Education Technology Leaders Home > Magazine > Archives > October 2002 > Feature > Copyright > Quiz Add to Reading List Source URL: www.halldavidson.org Language: English - Date: 2002-12-08 03:41:27 United States copyright law Yearbook Copyright law of the United States Copy protection Educational technology Knowledge Civil law Law Copyright law Fair use
533	Saturday, December 7, 2002 techLEARNING.com \| Technology & Learning - The Resource for Education Technology Leaders Home > Magazine > Archives > October 2002 > Feature > Copyright > Quiz Add to Reading List Source URL: www.halldavidson.org Language: English - Date: 2002-12-08 03:41:38 Information Fair use Copyright law of the United States Copyright Ripping Copyright law Law Civil law
534	2014 Seventh International Conference on Mobile Computing and Ubiquitous Networking (ICMU) Feature Selection for Floor-changing Activity Recognition in Multi-Floor Pedestrian Navigation Sara Khalifa∗‡ , Mahbub Hassa Add to Reading List Source URL: www.cse.unsw.edu.au Language: English - Date: 2014-01-23 19:11:42 Bayesian statistics Naive Bayes classifier Classifier Feature selection Weka Support vector machine Pattern recognition Perceptron Statistics Statistical classification Machine learning
535	1 Supplementary Discussion 1.1 Overview First, we report how well the trained classifiers could detect the presence of each image category (1.2), for both target and distractor stimuli. We then present feature importance Add to Reading List Source URL: compmem.princeton.edu Language: English - Date: 2010-11-04 09:54:31 Statistical classification Cognition Negative priming Mind Linguistics Philosophy of mind Parts of speech Machine learning Classifier
536	Springboard Questions and Answers 1. How do I access the glossary feature? The glossary feature is still available; it just has to be activated. This can be accomplished by editing the navigation bar. There is a handout Add to Reading List Source URL: blogs.uakron.edu Language: English - Date: 2011-04-21 11:46:57 Distance education Educational technology Academia Rubric Dropbox SpringBoard E-learning Resource fork Software Education Computing
537	JOURNAL OF LATEX CLASS FILES, VOL. 1, NO. 8, AUGUST[removed]Discriminative Feature Co-occurrence Selection for Object Detection Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2007-08-08 04:33:06 Statistical classification Learning Parts of speech Machine learning AdaBoost Classifier Boosting Naive Bayes classifier Support vector machine Statistics Ensemble learning Artificial intelligence
538	Active Feature-Value Acquisition for Classifier Induction Prem Melville Dept. of Computer Sciences Univ. of Texas at Austin [removed] Add to Reading List Source URL: www.cs.utexas.edu Language: English - Date: 2004-10-13 12:26:03 Measurement Psychometrics Supervised learning Cross-validation Accuracy and precision Uncertainty Statistics Machine learning Statistical theory
539	ESTIMATING PLANAR STRUCTURE IN SINGLE IMAGES BY LEARNING FROM EXAMPLES Osian Haines and Andrew Calway University of Bristol, UK {haines, andrew}@cs.bris.ac.uk Add to Reading List Source URL: www.cs.bris.ac.uk Language: English - Date: 2014-03-11 07:06:25 Histogram of oriented gradients Feature Orientation Object recognition Euclidean vector Vector space Histogram Segmentation Scale-invariant feature transform Computer vision Algebra Mathematics
540	Combining Randomization and Discrimination for Fine-Grained Image Categorization Bangpeng Yao∗ Aditya Khosla∗ Li Fei-Fei Computer Science Department, Stanford University, Stanford, CA Add to Reading List Source URL: vision.stanford.edu Language: English - Date: 2013-09-22 18:20:29 Statistics Computer vision Ensemble learning Binary trees Image processing Random forest Object recognition Feature selection Segmentation Machine learning Artificial intelligence Decision trees

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