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  Browse All Reviews > Computing Methodologies (I) > Artificial Intelligence (I.2) > Learning (I.2.6) > Concept Learning (I.2.6...)  
 
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  1-10 of 47 Reviews about "Concept Learning (I.2.6...)": Date Reviewed
  An approach of support approximation to discover frequent patterns from concept-drifting data streams based on concept learning
Li C., Jea K.  Knowledge and Information Systems 40(3): 639-671, 2014. Type: Article

Data stream mining is a variation of data mining with additional requirements: instead of looking for the proverbial needle in the haystack, the data miner is expected to inspect a never-ending stream of hay arriving on a conveyor belt. The notion...

Apr 29 2015
  Gestures that people can understand and use
Ardito C., Costabile M., Jetter H.  Journal of Visual Languages and Computing 25(5): 572-576, 2014. Type: Article

Computing devices often include sensors to track the body, limbs, hands, and fingers of a user. User interfaces rely on these capabilities to recognize gestures, and considerable energy is being devoted to the development of a core set of gestures...

Mar 26 2015
  On equivalence of conceptual scaling and generalized one-sided concept lattices
Butka P., Pócs J., Pócsová J.  Information Sciences 25957-70, 2014. Type: Article

A technical account with mathematical proofs, this paper shows that the methods of conceptual scaling and generalized one-sided concept lattices are equivalent....

Jun 3 2014
  Lessons in learning
Smith J.  IEEE MultiMedia 20(3): 2-3, 2013. Type: Article

It is a time-honored approach in machine intelligence to study the way humans manage intellectual tasks. In this two-page essay, Smith notes that the way machines learn still differs significantly from how humans and animals learn. He calls attent...

Apr 14 2014
  A scientific concept animation tool for content implementation in e-learning
Otoo E., Osikoya D.  SAICSIT 2012 (Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference, Pretoria, South Africa,  Oct 1-3, 2012) 243-252, 2012. Type: Proceedings

Otoo and Osikoya describe how to prepare animated presentations to be used in e-learning. The abstract states: “translate sequence tagged diagrams in 2D and 3D produced with Inkscape into Beamer frames with sequence diagrams with commands fo...

Jan 3 2013
  Model selection in reinforcement learning
Farahmand A., Szepesvári C.  Machine Learning 85(3): 299-332, 2011. Type: Article

This paper considers the problem of finding an optimal action-value function, and choosing the action to perform, in the context of batch reinforcement learning. The learning problem is to identify the best action to take in the context of a Marko...

May 3 2012
  Health: related information structuring for the semantic Web
Eljinini M.  ISWSA 2011 (Proceedings of the 2011 International Conference on Intelligent Semantic Web Services and Applications, Amman, Jordan,  Apr 18-20, 2011) 1-7, 2011. Type: Proceedings

Eljinini sets out to provide a methodology and software implementation to harness the exponential growth of Web-based information, particularly in the medical field. Rather than suggesting a major revamp of existing Web sites to make the Web site ...

Aug 9 2011
  Methods for finding frequent items in data streams
Cormode G., Hadjieleftheriou M.  The VLDB Journal: The International Journal on Very Large Data Bases 19(1): 3-20, 2010. Type: Article

A large class of real-world applications, with a tight relation to large-scale industrial systems, requires the processing of data that arrives in the form of streams. Data stream mining is a challenging research problem due to the high volume of ...

Aug 10 2010
  Using genetic algorithms to evolve a population of topical queries
Cecchini R., Lorenzetti C., Maguitman A., Brignole N.  Information Processing and Management: an International Journal 44(6): 1863-1878, 2008. Type: Article

This paper looks at a new mechanism using genetic algorithms (GAs) to generate an improved query for an information retrieval system. There has been some research on using GAs for generating better queries using user relevance feedback....

Dec 30 2008
  Learning in varieties of the form V*LI from positive data
García P., Ruiz J.  Theoretical Computer Science 362(1): 100-114, 2006. Type: Article

Grammatical inference refers to the process of learning grammars and languages from data. In literature, it refers to automata induction, grammar induction, and automatic language acquisition. There are applications for machine learning in syntact...

Aug 17 2007
 
 
 
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