Images as Symbols: An Associative Neurotransmitter-Field Model of the Brodmann Areas
Transactions on Computational Science V
Supporting Literature Exploration with Granular Knowledge Structures
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
A Two-Phase Model for Learning Rules from Incomplete Data
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Interpreting concept learning in cognitive informatics and granular computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Two-phase rule induction from incomplete data
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Mereological theories of concepts in granular computing
Transactions on computational science II
Concept-based learning of human behavior for customer relationship management
Information Sciences: an International Journal
An interval set model for learning rules from incomplete information table
International Journal of Approximate Reasoning
A Two-Phase Model for Learning Rules from Incomplete Data
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model
International Journal of Software Science and Computational Intelligence
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Concepts are the basic units of thought that under-lie human intelligence and communication. From the perspective of cognitive informatics, a layered framework is suggested for concept formation and learning. It combines cognitive science and machine learning approaches. The philosophical issues and various views of concepts are reviewed. Concept learning methods are presented based on the classical view of concepts.