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Algebraic approach to data processin...

Urenda, Julio C.

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Algebraic approach to data processing = techniques and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Algebraic approach to data processing/ by Julio C. Urenda, Vladik Kreinovich.
Reminder of title:	techniques and applications /
Author:	Urenda, Julio C.
other author:	Kreinovich, Vladik.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xiii, 250 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- What Are the Most Natural and the Most Frequent Transformations -- Which Functions and Which Families of Functions Are Invariant -- What Is the General Relation Between Invariance And Optimality -- General Application: Dynamical Systems -- First Application to Physics: Why Liquids? -- Second Application to Physics: Warping of Our Galaxy.
Contained By:	Springer Nature eBook
Subject:	Computer science - Mathematics. -
Online resource:	https://doi.org/10.1007/978-3-031-16780-5
ISBN:	9783031167805

Algebraic approach to data processing = techniques and applications /
Urenda, Julio C.

Algebraic approach to data processingtechniques and applications /[electronic resource] :by Julio C. Urenda, Vladik Kreinovich. - Cham :Springer International Publishing :2022. - xiii, 250 p. :ill., digital ;24 cm. - Studies in big data,v. 1152197-6511 ;. - Studies in big data ;v. 115..

Introduction -- What Are the Most Natural and the Most Frequent Transformations -- Which Functions and Which Families of Functions Are Invariant -- What Is the General Relation Between Invariance And Optimality -- General Application: Dynamical Systems -- First Application to Physics: Why Liquids? -- Second Application to Physics: Warping of Our Galaxy.

The book explores a new general approach to selecting-and designing-data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms. The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions. That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well. The book is recommended to researchers and practitioners who need to select a data processing technique-or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing.

ISBN: 9783031167805

Standard No.: 10.1007/978-3-031-16780-5doiSubjects--Topical Terms:

532725
Computer science
--Mathematics.

LC Class. No.: QA76.9.M35 / U74 2022

Dewey Class. No.: 004.0151

Algebraic approach to data processing = techniques and applications /
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245 1 0 $a Algebraic approach to data processing $h [electronic resource] : $b techniques and applications / $c by Julio C. Urenda, Vladik Kreinovich.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xiii, 250 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6511 ; $v v. 115
505 0 $a Introduction -- What Are the Most Natural and the Most Frequent Transformations -- Which Functions and Which Families of Functions Are Invariant -- What Is the General Relation Between Invariance And Optimality -- General Application: Dynamical Systems -- First Application to Physics: Why Liquids? -- Second Application to Physics: Warping of Our Galaxy.
520 $a The book explores a new general approach to selecting-and designing-data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms. The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions. That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well. The book is recommended to researchers and practitioners who need to select a data processing technique-or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing.
650 0 $a Computer science $x Mathematics. $3 532725
650 0 $a Electronic data processing. $3 520749
650 0 $a Computational intelligence. $3 595739
650 0 $a Big data. $3 2045508
650 1 4 $a Data Engineering. $3 3409361
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Big Data. $3 3134868
700 1 $a Kreinovich, Vladik. $3 1965595
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830 0 $a Studies in big data ; $v v. 115. $3 3607110
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