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Fundamentals of pattern recognition (2nd revised and expanded ed.)
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Rough-Neuro-Computing: Techniques for Computing with Words
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Rough Sets: Mathematical Foundations
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Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
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An inquiry into anatomy of conflicts
Information Sciences: an International Journal
On-Line elimination of non-relevant parts of complex objects in behavioral pattern identification
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Behavioral pattern identification through rough set modelling
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
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Transactions on Rough Sets III
Analogy-based reasoning in classifier construction
Transactions on Rough Sets IV
Rough sets and vague concept approximation: from sample approximation to adaptive learning
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Matching 2d image segments with genetic algorithms and approximation spaces
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Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Reinforcement Learning with Approximation Spaces
Fundamenta Informaticae
Calculi of Approximation Spaces
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
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Actor Critic Learning: A Near Set Approach
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
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MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Nature-inspired framework for measuring visual image resemblance: A near rough set approach
Theoretical Computer Science
Multi-valued approach to near set theory
Transactions on Rough Sets XV
Measuring Resemblances Between Swarm Behaviours: A Perceptual Tolerance Near Set Approach
Fundamenta Informaticae - Swarm Intelligence
Optimization in Discovery of Compound Granules
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Nearness of Objects: Extension of Approximation Space Model
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Development of Near Sets Within the Framework of Axiomatic Fuzzy Sets
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Towards concept anchoring for cognitive robots
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Transactions on Rough Sets XVI
Nearness of subtly different digital images
Transactions on Rough Sets XVI
Maximal clique enumeration in finding near neighbourhoods
Transactions on Rough Sets XVI
Sufficiently Near Neighbourhoods of Points in Flow Graphs. A Near Set Approach
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Fundamenta Informaticae - Concurrency, Specification and Programming
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The problem considered in this paper is how to approximate sets of objects that are qualitatively but not necessarily spatially near each other. The term qualitatively near is used here to mean closeness of descriptions or distinctive characteristics of objects. The solution to this problem is inspired by the work of Zdzisław Pawlak during the early 1980s on the classification of objects by means of their attributes. This article introduces a special theory of the nearness of objects that are either static (do not change) or dynamic (change over time). The basic approach is to consider a link relation, which is defined relative to measurements associated with features shared by objects independent of their spatial relations. One of the outcomes of this work is the introduction of new forms of approximations of objects and sets of objects. The nearness of objects can be approximated using rough set methods. The proposed approach to approximation of objects is a straightforward extension of the rough set approach to approximating objects, where approximation can be considered in the context of information granules (neighborhoods). In addition, the usual rough set approach to concept approximation has been enriched by an increase in the number of granules (neighborhoods) associated with the classification of a concept as near to its approximation. A byproduct of the proposed approximation method is what we call a near set. It should also be observed that what is presented in this paper is considered a special (not a general) theory about nearness of objects. The contribution of this article is an approach to nearness as a vague concept which can be approximated from the state of objects and domain knowledge.