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Kolmogorov Complexity in Perspective Part II: Classification, Information Processing and Duality

  • Marie Ferbus-ZandaEmail author
Chapter
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Part of the Logic, Epistemology, and the Unity of Science book series (LEUS, volume 34)

Abstract

We survey diverse approaches to the notion of information: from Shannon entropy to Kolmogorov complexity. Two of the main applications of Kolmogorov complexity are presented: randomness and classification. The survey is divided in two parts in the same volume.

Part II is dedicated to the relation between logic and information system, within the scope of Kolmogorov algorithmic information theory. We present a recent application of Kolmogorov complexity: classification using compression, an idea with provocative implementation by authors such as Bennett, Vitányi and Cilibrasi among others. This stresses how Kolmogorov complexity, besides being a foundation to randomness, is also related to classification. Another approach to classification is also considered: the so-called “Google classification”. It uses another original and attractive idea which is connected to the classification using compression and to Kolmogorov complexity from a conceptual point of view. We present and unify these different approaches to classification in terms of Bottom-Up versus Top-Down operational modes, of which we point the fundamental principles and the underlying duality. We look at the way these two dual modes are used in different approaches to information system, particularly the relational model for database introduced by Codd in the 1970s. These operational modes are also reinterpreted in the context of the comprehension schema of axiomatic set theory ZF. This leads us to develop how Kolmogorov’s complexity is linked to intensionality, abstraction, classification and information system.

Keywords

Relational Database Relational Schema Comprehension Schema Sequent Calculus Kolmogorov Complexity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

For Francine Ptakhine, who gave me liberty of thinking and writing.

Thanks to Serge Grigorieff and Chloé Ferbus for listening, fruitful communication and for the careful proofreading and thanks to Maurice Nivat who welcomed me at the LITP in 1983.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.LIAFA, CNRS & Université Paris Diderot – Paris 7Paris Cedex 13France

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