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Concept learning
Results: 882

#	Item
61	Coordination in Multiagent Reinforcement Learning: A Bayesian Approach Georgios Chalkiadakis Craig Boutilier Add to Reading List Source URL: www.intelligence.tuc.gr Language: English - Date: 2009-03-02 16:24:03 Game theory Reinforcement learning Nash equilibrium Q-learning Strategy Partially observable Markov decision process Action selection Best response Bellman equation Zero-sum game Agent-based model Solution concept
62	Synthetic Multi-label Data Streams Jesse Read, Bernhard Pfahringer, Geoff Holmes University of Waikato, Hamilton, New Zealand Feature Space Introduction Add to Reading List Source URL: users.ics.aalto.fi Language: English - Date: 2010-10-25 05:10:52 Machine learning Concept drift Data mining Slashdot Multi-label classification
63	Concepts and Categories Concepts and Categories II Informatics 1 CG: Lecture 16 Add to Reading List Source URL: www.inf.ed.ac.uk Language: English - Date: 2016-02-23 08:51:49 Semantics Cognition Learning theory Cognitive science Prototype theory Categorization Type theory Category of being Theory Exemplar theory Concept learning
64	Microsoft Word - STEM Topic Assessment and Evaluation Add to Reading List Source URL: stemgateway.unm.edu Language: English - Date: 2015-08-17 23:54:43 Educational psychology Educational practices Curricula Pedagogy Philosophy of education Active learning Educational technology Concept inventory Formative assessment Instructional design Scientific teaching Science education
65	AMELIE eLearning Concept Introduction Add to Reading List Source URL: www.ealt-amelie.eu Language: English - Date: 2014-03-12 16:26:49 Educational psychology Distance education Educational technology Pedagogy Education Virtual learning environment Blended learning Learning environment E-learning Learning Eight dimensional e-learning framework European Distance and E-learning Network
66	THE QUICK NOTES PHILOSOPHY The Theoty of Optimum Amounts There exists for every CONCEPT to be learned, an optimum amount of explanatory material. There exists for every TOPIC to be learned, an optimum number of concepts Add to Reading List Source URL: www.textbooksfree.org Language: English - Date: 2013-10-23 15:59:00 Educational psychology Intelligence Developmental psychology Learning Education theory Human intelligence G factor Educational technology Education Theory of multiple intelligences Skill
67	BY-LAWS SINTE GLESKA UNIVERSITY, INC. Sinte Gleska University, a tribally-chartered institution of higher learning, shall be governed in the manner of Wolakota, and its operation shall be consistent with the concept of s Add to Reading List Source URL: www.sintegleska.edu Language: English - Date: 2016-03-29 15:47:04 Lakota Rosebud Indian Reservation Sinte Gleska University Regents Examinations Brul Mission South Dakota Regent Regents of the University of California
68	A Catalog of Patterns for Concept Lattice Interpretation in Software Reengineering Muhammad U.Bhatti∗ , Nicolas Anquetil∗ , Marianne Huchard† , and St´ephane Ducasse∗ ∗ RMoD Project-Team INRIA - Lille Nord Eu Add to Reading List Source URL: rmod.inria.fr Language: English - Date: 2015-11-03 10:16:25 Lattice theory Lie groups Data mining Formal concept analysis Machine learning Ontology Complete lattice Lattice Object-oriented programming Algebraic structure Software design pattern Object Process Methodology
69	Effective Learning Through Concept Mapping Learning in medical school (and medical practice) requires the development of an understanding of patterns and relationships in order to be useful. Each step in learning involve Add to Reading List Source URL: www.ttuhsc.edu Language: English - Date: 2013-01-17 18:05:56 Knowledge representation Educational psychology Educational technology Diagrams Note-taking Concept map Learning Tag
70	A Bayesian Approach to Concept Drift Stephen H. Bach, Marcus A. Maloof Introduction Comparing Conditional Distributions Add to Reading List Source URL: stephenbach.net Language: English - Date: 2013-06-10 18:15:07 Bayesian statistics Statistical models Machine learning Prior probability Bayesian network Concept drift Probability distribution Naive Bayes classifier Ensemble learning

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