Patterns in numerical data: Practical approximations to Kolmogorov complexity

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1711, Issue , 1999, Pages 509-513

  Lin, T Y ^a,b  

^a SAN JOSE STATE UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ROUGH SET THEORY; SOFT COMPUTING;

KOLMOGOROV COMPLEXITY; NUMERICAL DATA; NUMERICAL SEQUENCE;

DATA MINING;

EID: 84958035045     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-48061-7_62     Document Type: Conference Paper

References (6)

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2. P. Domingos, Occam’s Two Razors: The Sharp and the Blunt, In: R. Agrawal, P Stolorz, and G. Piatetsky-Shapiro, the Proceedings of the Fourth International Conference on Knowledge Discovery & Data Mining, New York City, August 27-31, 1998.
3. T. Y. Lin,”Discovering Patterns in Numerical Sequences Using Rough Set Theory,” In: the Proceeding of the Third World Multi-conference on Systemics, Cybernetics and Informatics, Volume 5, Computer Science and Engineering. Orlando, Florida, July 31-August 4, 1999
4. Ming Li and P. M. Vitanyi, Komogorov Complexity and its Applications, in Handbook of Theoretical Computer Science J van Leeuwen
5. T. G. Dietterich and R. S. Michalski, Learning to Predict Sequences, In R. Micgalski, J. Carbonell, T. Michell(eds),Machine Learning: An Artificial Intelligeve Approach (Vol II,63-106), San Franciscom Morgan Kaufmann.
6. A. Mukherjea and K. Pothoven, Real and Functional Analysis, Plenum Press, New York, 1978