Formal learning

Results: 810



#Item
31Concept Similarity and Related Categories in SearchSleuth Frithjof Dau, Jon Ducrou and Peter Eklund , ,  School of Information Systems and Technology University of Wollo

Concept Similarity and Related Categories in SearchSleuth Frithjof Dau, Jon Ducrou and Peter Eklund , , School of Information Systems and Technology University of Wollo

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Source URL: www.dr-dau.net

Language: English - Date: 2016-06-19 12:16:28
32Research Corner: Work based Mentoring Dr. Jean Rhodes, University of Massachusetts – Boston, July 2003 The bottom line on work-based mentoring Informal and formal mentors have been heralded as among the key ingredients

Research Corner: Work based Mentoring Dr. Jean Rhodes, University of Massachusetts – Boston, July 2003 The bottom line on work-based mentoring Informal and formal mentors have been heralded as among the key ingredients

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Source URL: www.mentoring.org

Language: English - Date: 2016-03-22 09:33:15
33Learning from an expert’s proof AI4FM Leo Freitas and Cliff B Jones School of Computing Science, Newcastle University, UK, {leo.freitas, cliff.jones}@ncl.ac.uk Abstract

Learning from an expert’s proof AI4FM Leo Freitas and Cliff B Jones School of Computing Science, Newcastle University, UK, {leo.freitas, cliff.jones}@ncl.ac.uk Abstract

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Source URL: fm.csl.sri.com

Language: English - Date: 2010-11-02 19:53:10
34»Education for Change«  Combining History Learning and Human Rights Education in Formal, Non-formal and Higher Education  Book Launches & Symposium

»Education for Change« Combining History Learning and Human Rights Education in Formal, Non-formal and Higher Education Book Launches & Symposium

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Source URL: www.historyandhumanrights.de

Language: English - Date: 2016-07-21 06:47:14
35Learning Partially Observable Action Models: Efficient Algorithms Dafna Shahaf Allen Chang Eyal Amir Computer Science Department University of Illinois, Urbana-Champaign Urbana, IL 61801, USA {dshahaf2,achang6,eyal}@uiuc

Learning Partially Observable Action Models: Efficient Algorithms Dafna Shahaf Allen Chang Eyal Amir Computer Science Department University of Illinois, Urbana-Champaign Urbana, IL 61801, USA {dshahaf2,achang6,eyal}@uiuc

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Source URL: www.cs.huji.ac.il

Language: English - Date: 2010-08-28 23:41:19
36The rise of automatic feature-generation techniques, including deep learning, has the potential to greatly enlarge the pool of machinelearning users. Such methods require large labeled training sets to obtain high-qualit

The rise of automatic feature-generation techniques, including deep learning, has the potential to greatly enlarge the pool of machinelearning users. Such methods require large labeled training sets to obtain high-qualit

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Source URL: mmds-data.org

Language: English - Date: 2016-06-23 15:50:48
37SURVEY ON NON-FORMAL AND INFORMAL LEARNING ACTIVITIES IN PUBLIC LIBRARIES ACROSS EUROPE INTRODUCTION Welcome to the survey on non-formal and informal learning activities in public libraries across Europe! With your libra

SURVEY ON NON-FORMAL AND INFORMAL LEARNING ACTIVITIES IN PUBLIC LIBRARIES ACROSS EUROPE INTRODUCTION Welcome to the survey on non-formal and informal learning activities in public libraries across Europe! With your libra

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Source URL: www.eblida.org

Language: English - Date: 2016-05-31 12:14:57
38NEWS BRIEF JOS Hong Kong demonstrates big data analytics solution at Tableau Visual Analytics Forum Hong Kong – 27 July, 2016 – JOS Hong Kong participated in the Tableau Visual Analytics Forum on 21 JulyThe fo

NEWS BRIEF JOS Hong Kong demonstrates big data analytics solution at Tableau Visual Analytics Forum Hong Kong – 27 July, 2016 – JOS Hong Kong participated in the Tableau Visual Analytics Forum on 21 JulyThe fo

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Source URL: www.jos.com

Language: English - Date: 2016-08-09 03:08:52
392016 Qualifying Examination Topics and Points Summary List B1 Teachers

2016 Qualifying Examination Topics and Points Summary List B1 Teachers

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Source URL: tigpbp.iis.sinica.edu.tw

Language: English - Date: 2016-01-30 04:12:17
40UNCLASSIFIED  Grammatical Inference and Machine Learning Approaches to Post-Hoc LangSec Sheridan Curley and & Dr. Richard Harang (ARL)

UNCLASSIFIED Grammatical Inference and Machine Learning Approaches to Post-Hoc LangSec Sheridan Curley and & Dr. Richard Harang (ARL)

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Source URL: spw16.langsec.org

Language: English - Date: 2016-06-06 10:35:22