Support vector machine

Results: 2011



#Item
21Collective Activity Detection using Hinge-loss Markov Random Fields Ben London, Sameh Khamis, Stephen H. Bach, Bert Huang, Lise Getoor, Larry Davis University of Maryland College Park, MD 20742 {blondon,sameh,bach,bert,g

Collective Activity Detection using Hinge-loss Markov Random Fields Ben London, Sameh Khamis, Stephen H. Bach, Bert Huang, Lise Getoor, Larry Davis University of Maryland College Park, MD 20742 {blondon,sameh,bach,bert,g

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Source URL: psl.umiacs.umd.edu

Language: English - Date: 2013-06-14 19:26:52
22ABOUT MANUSCRIPTS FOR IJ ITA

ABOUT MANUSCRIPTS FOR IJ ITA

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Source URL: foibg.com

Language: English - Date: 2015-02-02 08:46:59
23Support Vector Machine The Linearly Non-Separable Case Ling Zhu  Fall 2013

Support Vector Machine The Linearly Non-Separable Case Ling Zhu Fall 2013

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Source URL: www.pstat.ucsb.edu

Language: English - Date: 2014-11-07 15:19:49
24A Few Useful Things to Know about Machine Learning Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA, U.S.A.

A Few Useful Things to Know about Machine Learning Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA, U.S.A.

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Source URL: homes.cs.washington.edu

Language: English - Date: 2015-08-13 22:08:12
25This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOITPAMI, IEEE T

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOITPAMI, IEEE T

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Source URL: www2.ece.ohio-state.edu

Language: English - Date: 2016-03-04 15:36:09
26264  Genome Informatics 13: 264–Characteristics of Support Vector Machines in Gene Expression Analysis

264 Genome Informatics 13: 264–Characteristics of Support Vector Machines in Gene Expression Analysis

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Source URL: www.jsbi.org

Language: English - Date: 2002-12-19 23:03:34
27Streaming Multi-label Classification Jesse Read† , Albert Bifet, Geoff Holmes, Bernhard Pfahringer University of Waikato, Hamilton, New Zealand †

Streaming Multi-label Classification Jesse Read† , Albert Bifet, Geoff Holmes, Bernhard Pfahringer University of Waikato, Hamilton, New Zealand †

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Source URL: users.ics.aalto.fi

Language: English - Date: 2011-10-17 14:24:14
28

PDF Document

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Source URL: www.work.caltech.edu

Language: English - Date: 2015-01-01 11:20:46
29Optimal Gradient-Based Learning Using Importance Weights Sepp Hochreiter and Klaus Obermayer Bernstein Center for Computational Neuroscience and Technische Universit¨at Berlin

Optimal Gradient-Based Learning Using Importance Weights Sepp Hochreiter and Klaus Obermayer Bernstein Center for Computational Neuroscience and Technische Universit¨at Berlin

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Source URL: www.bioinf.jku.at

Language: English - Date: 2013-01-23 02:39:45
30Feature Selection and Classification on Matrix Data: From Large Margins To Small Covering Numbers Sepp Hochreiter and Klaus Obermayer Department of Electrical Engineering and Computer Science

Feature Selection and Classification on Matrix Data: From Large Margins To Small Covering Numbers Sepp Hochreiter and Klaus Obermayer Department of Electrical Engineering and Computer Science

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Source URL: www.bioinf.jku.at

Language: English - Date: 2011-08-11 02:12:59