Machine learning

Results: 10129



#Item
921A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints Machine learning for systems Nikita Mishra, Harper Zhang , John Lafferty & Henry Hoffmann Department of Computer Science,

A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints Machine learning for systems Nikita Mishra, Harper Zhang , John Lafferty & Henry Hoffmann Department of Computer Science,

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Source URL: people.cs.uchicago.edu

Language: English - Date: 2015-07-12 23:42:34
922Machine recognition of timbre using steady-state tone of acoustic musical instruments Ichiro Fujinaga Peabody Conservatory of Music Johns Hopkins University Baltimore, MD USA 21202

Machine recognition of timbre using steady-state tone of acoustic musical instruments Ichiro Fujinaga Peabody Conservatory of Music Johns Hopkins University Baltimore, MD USA 21202

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Source URL: www.music.mcgill.ca

Language: English - Date: 1999-03-18 22:07:48
923Generating Synthetic Multi-label Data Streams Jesse Read, Bernhard Pfahringer, Geoff Holmes Department of Computer Science The University of Waikato Hamilton, New Zealand {jmr30,bernhard,geoff}@cs.waikato.ac.nz

Generating Synthetic Multi-label Data Streams Jesse Read, Bernhard Pfahringer, Geoff Holmes Department of Computer Science The University of Waikato Hamilton, New Zealand {jmr30,bernhard,geoff}@cs.waikato.ac.nz

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Source URL: users.ics.aalto.fi

Language: English - Date: 2010-10-25 05:10:52
9249. KONFERENCA JEZIKOVNE TEHNOLOGIJE Informacijska družba - IS 2014 9th Language Technologies Conference Information Society - IS 2014

9. KONFERENCA JEZIKOVNE TEHNOLOGIJE Informacijska družba - IS 2014 9th Language Technologies Conference Information Society - IS 2014

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Source URL: nl.ijs.si

Language: English - Date: 2014-10-19 11:19:14
925Prediction tasks over nodes and edges in networks require careful effort in engineering features for learning algorithms. Recent research in the broader field of representation learning has led to significant progress in

Prediction tasks over nodes and edges in networks require careful effort in engineering features for learning algorithms. Recent research in the broader field of representation learning has led to significant progress in

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Source URL: mmds-data.org

Language: English - Date: 2016-06-23 15:50:48
926NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning, Whistler, Canada, DecemberInvestigating Convergence of Restricted Boltzmann Machine Learning  Hannes Schulz

NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning, Whistler, Canada, DecemberInvestigating Convergence of Restricted Boltzmann Machine Learning Hannes Schulz

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Source URL: amueller.github.io

Language: English - Date: 2016-08-04 15:59:56
927192  Genome Informatics 13: 192–Using Feature Generation and Feature Selection for Accurate Prediction of Translation Initiation Sites

192 Genome Informatics 13: 192–Using Feature Generation and Feature Selection for Accurate Prediction of Translation Initiation Sites

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Source URL: www.jsbi.org

Language: English - Date: 2002-12-09 05:53:41
928Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning Yulia Tsvetkov♠ Manaal Faruqui♠ Wang Ling♣ Brian MacWhinney♠ Chris Dyer♣♠ ♠ Carnegie Mellon University ♣ G

Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning Yulia Tsvetkov♠ Manaal Faruqui♠ Wang Ling♣ Brian MacWhinney♠ Chris Dyer♣♠ ♠ Carnegie Mellon University ♣ G

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Source URL: www.aclweb.org

Language: English - Date: 2016-08-01 10:37:36
929Language Learning  ISSNStatistical Learning and Language: An Individual Differences Study

Language Learning ISSNStatistical Learning and Language: An Individual Differences Study

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Source URL: cnl.psych.cornell.edu

Language: English - Date: 2012-02-26 09:34:45
930Learning Extended Tree Augmented Naive StructuresI Cassio P. de Camposa,∗, Giorgio Coranib , Mauro Scanagattab , Marco Cuccuc , Marco Zaffalonb b Istituto a Queen’s University Belfast, UK Dalle Molle di Studi sull’

Learning Extended Tree Augmented Naive StructuresI Cassio P. de Camposa,∗, Giorgio Coranib , Mauro Scanagattab , Marco Cuccuc , Marco Zaffalonb b Istituto a Queen’s University Belfast, UK Dalle Molle di Studi sull’

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Source URL: ipg.idsia.ch

Language: English - Date: 2016-03-17 09:50:40