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Ranking SVM
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#	Item
1	Microsoft Word - LR4IR2009.v4-camera-nomark.doc Add to Reading List Source URL: www.sogou.com Language: English - Date: 2016-06-28 04:30:57 Machine learning Learning Learning to rank Ranking SVM Support vector machine PageRank Ranking Stability Document retrieval Information retrieval Feature selection Supervised learning
2	Nonlinear Feature Selection with the Potential Support Vector Machine Sepp Hochreiter and Klaus Obermayer Technische Universit¨ at Berlin Fakult¨ Add to Reading List Source URL: www.bioinf.jku.at Language: English - Date: 2013-01-23 02:44:45 Support vector machines Statistical classification Feature selection Sequential minimal optimization Sepp Hochreiter Vector space Duality Least squares support vector machine Ranking SVM
3	Metric Learning to Rank Brian McFee Department of Computer Science and Engineering, University of California, San Diego, CAUSA Gert Lanckriet Add to Reading List Source URL: eceweb.ucsd.edu Language: English - Date: 2015-07-31 19:00:27 Machine learning Search algorithms Structured prediction Learning Learning to rank Support vector machine K-nearest neighbors algorithm Ranking SVM
4	Perturbation based Large Margin Approach for Ranking Eunho Yang University of Texas at Austin Ambuj Tewari Add to Reading List Source URL: dept.stat.lsa.umich.edu Language: English - Date: 2012-09-12 18:50:24 Machine learning Learning Artificial intelligence Support vector machines Learning to rank Hinge loss Ranking SVM Structured support vector machine Empirical risk minimization Mathematical optimization Supervised learning Distribution
5	Noname manuscript No. (will be inserted by the editor) Learning to Rank with (a Lot of ) Word Features Bing Bai · Jason Weston · David Grangier · Ronan Collobert · Kunihiko Sadamasa · Add to Reading List Source URL: ronan.collobert.com Language: English - Date: 2009-09-30 13:22:01 Vector space model Learning to rank Ranking SVM Tf*idf Hash function Search engine indexing N-gram Supervised learning Latent semantic indexing Information science Information retrieval Science
6	0 Large Linear Classification When Data Cannot Fit In Memory Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang and Chih-Jen Lin, Department of Computer Science, National Taiwan University Recent advances in linear classificati Add to Reading List Source URL: www.csie.ntu.edu.tw Language: English - Date: 2011-11-04 17:52:46 Operations research Statistical classification Support vector machine Mathematical optimization Stochastic gradient descent Algorithm Gradient descent Sequential minimal optimization Ranking SVM Statistics Numerical analysis Mathematics
7	Ranking Specialization for Web Search: A Divide-and-Conquer Approach by Using Topical RankSVM Jiang Bian ∗ Add to Reading List Source URL: labs.yahoo.com Language: English - Date: 2013-04-05 04:39:41 Learning to rank Ranking function Vector space model Discounted cumulative gain Ranking Bin Ranking SVM Relevance feedback Information science Information retrieval Web query classification
8	Efficient Multiple-Click Models in Web Search ∗ Fan Guo Carnegie Mellon University Add to Reading List Source URL: www.wsdm2009.org Language: English - Date: 2009-04-14 09:41:44 Point and click Ranking SVM Web search query DOM events Information science Information retrieval Learning to rank
9	Microsoft Word - LR4IR2009.v4-camera-nomark.doc Add to Reading List Source URL: www.thuir.cn Language: English - Date: 2009-06-27 05:08:51 Machine learning Learning Learning to rank Searching Link analysis Ranking SVM PageRank Ranking function Feature selection Information science Information retrieval Statistics
10	Functional Genomics Workshop October, 2014 Clustering, SVM, MDS, ranking, heat maps, networks, Add to Reading List Source URL: docs.orange.biolab.si Language: English - Date: 2014-11-07 08:10:04 GUI widget Widget Random forest Data mining Scatter plot Plot Graphical user interfaces Statistics Software

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