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Representing cognitive processes remains one of the great research challenges. Many important application areas, such as clinical diagnosis, operate in an environment of relative magnitudes, counts, shapes, colours, etc. which are not well captured by current representational approaches. This paper presents conceptual spaces as a meso level representation(More)
We have introduced the linguistic weighted power mean and its computation via a generalization of the Karnick-Mendel algorithm. This new family of aggregation operators admits interval type-2 fuzzy membership functions for both its inputs and its weights to account for imprecise knowledge of these quantities. The simplest member of this family, the(More)
This paper presents a conceptual system based on two independently developed extensions of Gardenförs’ formulation of conceptual spaces. The new approach continues to emphasize the role of properties and their associations in conceptual representation, and to recognize the importance of similarity judgments in reasoning tasks. In the new theory, domains are(More)
The information in data depends on the subjective value system that the receiver of the data uses to interpret them. This paper looks at the information in a theory of ® rst order logic (a knowledge base) from the perspective of a decision maker for whom the validation of formulae (facts and rules) have varying importance. The decision maker’ s preferences(More)
This paper presents a conceptual system in which concepts are defined by binary associations between properties. Properties are measurable membership functions, defined on sets equipped with a measure that is the disjoint domains of representation. Instances of concepts (observations) are sets of points from these domains. Requiring properties to be(More)
Classification assigns an entity to a category on the basis of feature values encoded from a stimulus. Provided they are presented with sufficient training data, inductive classifier builders such as C4.5 are limited by encoding deficiencies and noise in the data, rather than by the method of deciding the category. However, such classification techniques do(More)