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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) (1999) 1711 509-513
DOI: 10.1007/978-3-540-48061-7_62
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Abstract

Intuitively, patterns of numerical sequences are often interpreted as formulas. However, we observed earlier that such an intuition is too naive. Notions analogous to Kolmogorov complexity theory are introduced. Based on these new formulations, a formula is a pattern only if its pattern complexity is simpler than the complexity of data.

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Lin, T. Y. (1999). Patterns in numerical data: Practical approximations to Kolmogorov complexity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1711, pp. 509–513). Springer Verlag. https://doi.org/10.1007/978-3-540-48061-7_62

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