Artificial neural networks

Results: 1630



#Item
991Improving the Separability of a Reservoir Facilitates Learning Transfer David Norton and Dan Ventura Computer Science Department Brigham Young University [removed], [removed]

Improving the Separability of a Reservoir Facilitates Learning Transfer David Norton and Dan Ventura Computer Science Department Brigham Young University [removed], [removed]

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Source URL: aaai.org

Language: English - Date: 2008-06-14 21:48:10
992Microsoft PowerPoint - Using artificial neural networks for heat and electric load forecasting.ppt

Microsoft PowerPoint - Using artificial neural networks for heat and electric load forecasting.ppt

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Source URL: www.oscogen.ethz.ch

Language: English - Date: 2003-02-12 10:46:42
993In The Autonomous Agents and Multi-Agent Systems Conference (AAMAS-07), Honolulu, Hawaii, May[removed]Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning Matthew E. Taylor, Shimon Whiteson, and Peter S

In The Autonomous Agents and Multi-Agent Systems Conference (AAMAS-07), Honolulu, Hawaii, May[removed]Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning Matthew E. Taylor, Shimon Whiteson, and Peter S

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Source URL: www.cs.utexas.edu

Language: English - Date: 2007-01-31 13:20:41
994Pomerleau, D.A[removed]Efficient Training of Artificial Neural Networks for Autonomous Navigation. In Neural Computation3:1 pp[removed]Efficient Training of Artificial Neural Networks for Autonomous Navigation

Pomerleau, D.A[removed]Efficient Training of Artificial Neural Networks for Autonomous Navigation. In Neural Computation3:1 pp[removed]Efficient Training of Artificial Neural Networks for Autonomous Navigation

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Source URL: www.ri.cmu.edu

Language: English - Date: 2008-10-18 21:10:31
995Extracting Propositional Rules from Feed-forward Neural Networks — A New Decompositional Approach Sebastian Bader and Steffen H¨olldobler and Valentin Mayer-Eichberger International Center for Computational Logic Tech

Extracting Propositional Rules from Feed-forward Neural Networks — A New Decompositional Approach Sebastian Bader and Steffen H¨olldobler and Valentin Mayer-Eichberger International Center for Computational Logic Tech

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Source URL: ceur-ws.org

Language: English - Date: 2006-11-03 20:49:32
996Microsoft Word - Document1

Microsoft Word - Document1

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Source URL: mfr.edp-open.org

Language: English
997COOPER UNION FOR THE ADVANCEMENT OF SCIENCE AND ART ALBERT NERKEN SCHOOL OF ENGINEERING Applications of Convolutional Neural Networks to Facial Detection and Recognition for Augmented

COOPER UNION FOR THE ADVANCEMENT OF SCIENCE AND ART ALBERT NERKEN SCHOOL OF ENGINEERING Applications of Convolutional Neural Networks to Facial Detection and Recognition for Augmented

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Source URL: www.cemetech.net

Language: English - Date: 2010-05-04 21:11:37
998ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky Ilya Sutskever Geoffrey Hinton University of Toronto

ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky Ilya Sutskever Geoffrey Hinton University of Toronto

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Source URL: image-net.org

Language: English - Date: 2014-02-08 22:41:26
999Likelihood Ratios and Recurrent Random Neural Networks in Detection of Denial of Service Attacks ¨ ¨ Georgios Loukas and Gulay Oke

Likelihood Ratios and Recurrent Random Neural Networks in Detection of Denial of Service Attacks ¨ ¨ Georgios Loukas and Gulay Oke

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Source URL: san.ee.ic.ac.uk

Language: English - Date: 2007-04-20 14:37:56
10001  A Biologically Inspired Denial of Service Detector Using the Random Neural Network ¨ Georgios Loukas and G¨ulay Oke

1 A Biologically Inspired Denial of Service Detector Using the Random Neural Network ¨ Georgios Loukas and G¨ulay Oke

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Source URL: san.ee.ic.ac.uk

Language: English - Date: 2007-05-17 12:59:54