McNaughton theorem of fuzzy logic from a data-mining point of view

Title:

McNaughton theorem of fuzzy logic from a data-mining point of view

Creator:

Holeňa, Martin

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:98fa65fa-374f-4753-953c-6a5997b47980
uuid:98fa65fa-374f-4753-953c-6a5997b47980

Subject:

Łukasiewicz logic, McNaughton theorem, data mining, rules extraction from data, and piecewise-linear neural networks

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The paper recalls the McNaughton theorem of fuzzy logic and the algorithms underlying its constructive proofs. It then shows how those algorithms can be combined with the algorithm underlying recent extension of the theorem to piecewise-linear functions with rational coefficients, and points out potential importance of the resulting combined algorithm for data mining. That result is immediately weakened through a complexity analysis of the algorithm that reveals that its worst-case complexity is doubly-exponential.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2007 Volume:17 | Number:3

Harvested from:

CDK

Metadata only:

false