Variable precision rough set model
Journal of Computer and System Sciences
Advances in the Dempster-Shafer theory of evidence
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Constraint Based Incremental Learning of Classification Rules
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Fundamenta Informaticae - Contagious Creativity - In Honor of the 80th Birthday of Professor Solomon Marcus
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Hierarchical Classifiers for Complex Spatio-temporal Concepts
Transactions on Rough Sets IX
Rough sets and vague concept approximation: from sample approximation to adaptive learning
Transactions on Rough Sets V
Arrow decision logic for relational information systems
Transactions on Rough Sets V
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We present a rough set approach to vague concept approximation within the adaptive learning framework. In particular, the role of extensions of approximation spaces in searching for concept approximation is emphasized. Boundary regions of approximated concepts within the adaptive learning framework are satisfying the higher order vagueness condition, i.e., the boundary regions of vague concepts are not crisp. There are important consequences of the presented framework for research on adaptive approximation of vague concepts and reasoning about approximated concepts. An illustrative example is included showing the application of Boolean reasoning in adaptive learning.