Add to ORKGAbstract. Approximation spaces are fundamental for the rough set ap-proach. We discuss their application in machine learning and pattern recognition
Covering approximation spaces is a class of important generalization of approximation spaces. For a ...
Abstract — An important topic of rough set theory is the approximation of undefinable sets or concep...
AbstractThe concept of approximation spaces is a key notion of rough set theory, which is an importa...
Abstract. In the paper we discuss approximation spaces relevant for rough-neuro computing
Abstract. This paper considers the problem of how to establish calculi of approximation spaces. Appr...
Adopting Zakowski-s upper approximation operator C and lower approximation operator C, this paper in...
AbstractIn this paper a generalized notion of an approximation space is considered. By an approximat...
Abstract — In this paper we propose that a vague set can be approximated by two vague sets in Pawlak...
Using the notion of preconcept, we generalize Pawlak’s approximation operators from a one-dimensiona...
Abstract. A rough set is a formal approximation of a crisp set which gives lower and upper approxima...
AbstractBased on the Pawlak rough set theory, this paper investigates separations in covering approx...
This paper concerns the latter approach. For studying on the approximate learning, it is important t...
In modeling multiagent systems for real-life problems, techniques for approximate reasoning about va...
Rough approximation operators in approximation spaces are the core concept of rough set theory, whic...
AbstractThe original rough set model was developed by Pawlak, which is mainly concerned with the app...
Covering approximation spaces is a class of important generalization of approximation spaces. For a ...
Abstract — An important topic of rough set theory is the approximation of undefinable sets or concep...
AbstractThe concept of approximation spaces is a key notion of rough set theory, which is an importa...
Abstract. In the paper we discuss approximation spaces relevant for rough-neuro computing
Abstract. This paper considers the problem of how to establish calculi of approximation spaces. Appr...
Adopting Zakowski-s upper approximation operator C and lower approximation operator C, this paper in...
AbstractIn this paper a generalized notion of an approximation space is considered. By an approximat...
Abstract — In this paper we propose that a vague set can be approximated by two vague sets in Pawlak...
Using the notion of preconcept, we generalize Pawlak’s approximation operators from a one-dimensiona...
Abstract. A rough set is a formal approximation of a crisp set which gives lower and upper approxima...
AbstractBased on the Pawlak rough set theory, this paper investigates separations in covering approx...
This paper concerns the latter approach. For studying on the approximate learning, it is important t...
In modeling multiagent systems for real-life problems, techniques for approximate reasoning about va...
Rough approximation operators in approximation spaces are the core concept of rough set theory, whic...
AbstractThe original rough set model was developed by Pawlak, which is mainly concerned with the app...
Covering approximation spaces is a class of important generalization of approximation spaces. For a ...
Abstract — An important topic of rough set theory is the approximation of undefinable sets or concep...
AbstractThe concept of approximation spaces is a key notion of rough set theory, which is an importa...