Titlebook: Rough Sets; Theoretical Aspects Zdzisław Pawlak Book 1991 Springer Science+Business Media Dordrecht 1991 Equivalence.Pattern Recognition.a

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必死

Imprecise Categories, Approximations and Rough Setss which can be worded using knowledge available in a given knowledge base. Certainly some categories can be definable in one knowledge base but undefinable in another one. Thus, if a category is not definable in a given knowledge base, the question arises whether it can be defined “approximately” in

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Switching Circuitssee the enclosed references, and for overview see for example Muroga (1979)). Our claim is that the methods we have just reported in the preceding several chapters can be successfully employed in this area. The best way to begin the discussion is to note that switching circuits performance can be pr

dura-mater

Machine Learnings a variety of approaches to machine learning, however by now no commonly accepted theoretical foundations has been developed. It seems that the rough set approach, can be used as a theoretical basis for some problems in machine learning. In this section we are going to outline briefly some ideas sh

深陷

myriad

Imprecise Categories, Approximations and Rough Setsnable in another one. Thus, if a category is not definable in a given knowledge base, the question arises whether it can be defined “approximately” in the knowledge base. In other words, we want to address here the central point of our approach, the vague categories.

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Reasoning about Knowledgekarelov (1981, 1989, 1991a,b,c), Wasilewska (1988a,b,c, 1989a,b,c) and others). Most of the above mentioned logical research has been directed to create deductive logical tools to deal with approximate (deductive) reasoning.