Rough sets: probabilistic versus deterministic approach
Machine learning and uncertain reasoning
Variable precision rough set model
Journal of Computer and System Sciences
Advances in the Dempster-Shafer theory of evidence
The nature of statistical learning theory
The nature of statistical learning theory
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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
A note on 3-valued rough logic accepting decision rules
Fundamenta Informaticae
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2003)
Rough sets and information granulation
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
Approximate Reasoning in MAS: Rough Set Approach
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Reinforcement Learning with Approximation Spaces
Fundamenta Informaticae
Near Sets. Special Theory about Nearness of Objects
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Approximate Reasoning in MAS: Rough Set Approach
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Hierarchical Classifiers for Complex Spatio-temporal Concepts
Transactions on Rough Sets IX
Paradigms of Denotational Mathematics for Cognitive Informatics and Cognitive Computing
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)
Interactive Granular Computing in Rightly Judging Systems
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Toward Rough-Granular Computing
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Pawlak's landscaping with rough sets
Transactions on rough sets VI
Toward perception based computing: a rough-granular perspective
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Research on rough set theory and applications in China
Transactions on rough sets VIII
Transactions on rough sets XII
Rough sets and higher order vagueness
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Transactions on Rough Sets IV
Rough sets and vague concept approximation: from sample approximation to adaptive learning
Transactions on Rough Sets V
Matching 2d image segments with genetic algorithms and approximation spaces
Transactions on Rough Sets V
Paradigms of Denotational Mathematics for Cognitive Informatics and Cognitive Computing
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)
Dialectics of counting and the mathematics of vagueness
Transactions on Rough Sets XV
A characterization of rough separability
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Reinforcement Learning with Approximation Spaces
Fundamenta Informaticae
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The approximation space definition has evolved in rough set theory over the last 15 years. The aim was to build a unified framework for concept approximations. We present an overview of this evolution together with some operations on approximation spaces that are used in searching for relevant approximation spaces. Among such operations are inductive extensions and granulations of approximation spaces. We emphasize important consequences of the paper for research on approximation of vague concepts and reasoning about them in the framework of adaptive learning. This requires developing new approach to vague concepts going beyond the traditional rough or fuzzy approaches.