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Classification in machine learning
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#	Item
231	Refined Experts Improving Classification in Large Taxonomies Paul N. Bennett Microsoft Research One Microsoft Way Redmond, WA Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2009-07-08 19:55:20 Support vector machine Hierarchy Tree structure Machine learning Linear classifier Binary classification Margin classifier Statistics Statistical classification Hierarchical classifier
232	Para-cycling Classifier Fact sheet What is Classification? Paralympic sport exists so that athletes with disabilities have equal opportunities to compete and be successful in sport. Classification groups athletes who hav Add to Reading List Source URL: www.sports.org.au Language: English - Date: 2014-01-23 19:15:01 Classifier Swimming at the Summer Paralympics Machine learning Statistical classification
233	20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013 www.mssanz.org.au/modsim2013 A Naive Bayes classifier for modeling distributions of the common reed in Southern Finland A. Add to Reading List Source URL: www.mssanz.org.au Language: English - Date: 2013-11-19 22:09:45 Botany Bayesian statistics Machine learning Naive Bayes classifier Phragmites Classifier Statistics Flora Statistical classification
234	2011 IEEE International Conference on Robotics and Automation Shanghai International Conference Center May 9-13, 2011, Shanghai, China Multi-Class Classification of Vegetation in Natural Environments Using an Unmanned Ae Add to Reading List Source URL: www-personal.acfr.usyd.edu.au Language: English - Date: 2015-01-05 23:35:42 Bayesian statistics Neural networks Support vector machine Naive Bayes classifier Linear discriminant analysis Multiclass classification Pattern recognition Normal distribution Artificial neural network Statistics Statistical classification Machine learning
235	Machine Learning, 46, 161–190, 2002 c 2002 Kluwer Academic Publishers. Manufactured in The Netherlands. Training Invariant Support Vector Machines DENNIS DECOSTE Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2006-02-01 17:05:02 Statistical classification Linear algebra Support vector machine Signal processing Fisher kernel Positive-definite kernel Kernel Linux kernel Vector space Algebra Statistics Machine learning
236	CI 2467 R Harvey Full name: Robert Harvey Proposal 5–1: Agree Proposal 5–2: For (a) (b) & (c) Agree Add to Reading List Source URL: www.alrc.gov.au Language: English - Date: 2011-11-16 23:50:12 Australian Classification Board Motion picture rating system Statistical classification Classifier E certificate Artificial intelligence Legal status of Internet pornography Censorship in Australia Censorship Machine learning
237	Automated approaches for detecting integration in student essays Simon Hughes1 ? , Peter Hastings1 , Joe Magliano2 , Susan Goldman3 , and Kim Lawless3 2 3 Add to Reading List Source URL: www.projectreadi.org Language: English - Date: 2014-10-31 15:05:59 Natural language processing Computational linguistics Statistical classification Data mining Support vector machine Document classification Latent semantic analysis Document-term matrix Binary classification Statistics Artificial intelligence Machine learning
238	Machine Learning, 46, 161–190, 2002 c 2002 Kluwer Academic Publishers. Manufactured in The Netherlands. Training Invariant Support Vector Machines DENNIS DECOSTE Add to Reading List Source URL: www.cs.berkeley.edu Language: English - Date: 2004-02-15 20:47:31 Statistical classification Linear algebra Support vector machine Signal processing Fisher kernel Positive-definite kernel Kernel Linux kernel Vector space Algebra Statistics Machine learning
239	Discovering Efficiency in Coarse-To-Fine Texture Classification Jonathan T. Barron and Jitendra Malik Computer Science Division University of California at Berkeley, Berkeley, CA, 94720 {barron, malik}@eecs.berkeley.edu Add to Reading List Source URL: www.cs.berkeley.edu Language: English - Date: 2014-04-01 23:51:58 Science Computer vision Image processing Machine learning Graphical models Maximum-entropy Markov model Segmentation Conditional random field Hidden Markov model Statistics Markov models Applied mathematics
240	¨ t Augsburg Universita Towards Learning with Objects in a Hierarchical Representation Nicolas Cebron Add to Reading List Source URL: www.multimedia-computing.de Language: English - Date: 2011-06-17 18:04:39 Artificial intelligence Support vector machine Supervised learning Margin classifier Binary classification Active learning Boosting methods for object categorization Linear classifier Statistics Machine learning Statistical classification

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