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Titlebook: Methods and Applications of Algorithmic Complexity; Beyond Statistical L Hector Zenil,Fernando Soler Toscano,Nicolas Gauvri Book 2022 Sprin

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書(shū)目名稱(chēng)Methods and Applications of Algorithmic Complexity
副標(biāo)題Beyond Statistical L
編輯Hector Zenil,Fernando Soler Toscano,Nicolas Gauvri
視頻videohttp://file.papertrans.cn/633/632200/632200.mp4
概述Provides recent research on evaluating the Kolmogorov complexity of short strings.Presents an alternative to compression algorithms.Written by leading experts in the field
叢書(shū)名稱(chēng)Emergence, Complexity and Computation
圖書(shū)封面Titlebook: Methods and Applications of Algorithmic Complexity; Beyond Statistical L Hector Zenil,Fernando Soler Toscano,Nicolas Gauvri Book 2022 Sprin
描述.This book explores a different pragmatic approach to algorithmic complexity rooted or motivated by the theoretical foundations of algorithmic probability and explores the relaxation of necessary and sufficient conditions in the pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance and applicability...Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently coexist for the first time, ranging from the dominant ones based upon popular statistical lossless compression algorithms (such as LZW) to newer approaches that advance, complement, and also pose their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented, and despite their many challenges, some of these methods are better grounded in or motivated by the principles of algorithmic information.?..The authors propose that the field can make greater contributions to science, causation, scientific discovery, networks, and cognition, to mention a few among many fields, instead of remaining either as a technical curiosity of
出版日期Book 2022
關(guān)鍵詞Complexity; Compression Algorithms; Kolmogorov Complexity; Short Strings; Algorithmic Complexity; Lossles
版次1
doihttps://doi.org/10.1007/978-3-662-64985-5
isbn_ebook978-3-662-64985-5Series ISSN 2194-7287 Series E-ISSN 2194-7295
issn_series 2194-7287
copyrightSpringer-Verlag GmbH Germany, part of Springer Nature 2022
The information of publication is updating

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2194-7287 by leading experts in the field.This book explores a different pragmatic approach to algorithmic complexity rooted or motivated by the theoretical foundations of algorithmic probability and explores the relaxation of necessary and sufficient conditions in the pursuit of numerical applicability, with
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Enumerating and Simulating Turing Machinesrter to longer) to produce an empirical estimation of the so-called universal distribution?[., .] related to algorithmic probability?[., .]. To this end, we have chosen the most standard and studied model of computation, that of Turing machines?[.] as used in the ‘Busy Beaver game’?[.].
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The Block Decomposition Methodan be viewed as a function that maps data onto other data using the same units or alphabet (if the translation is into different units or a larger or smaller alphabet, then the process is called an encoding).
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Validation and Generalization of CTMThis chapter introduces a generalization of CTM to higher dimension and thus to tensors. It requires an extension of Turing machines capable of operating on a 2-dimensional tape (by simply moving up and down in addition to the traditional left and right movements of the head).
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