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020 _a9783031167805
_9978-3-031-16780-5
024 7 _a10.1007/978-3-031-16780-5
_2doi
050 4 _aTA345-345.5
072 7 _aUN
_2bicssc
072 7 _aCOM018000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a620.00285
_223
100 1 _aUrenda, Julio C.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aAlgebraic Approach to Data Processing
_h[electronic resource] :
_bTechniques and Applications /
_cby Julio C. Urenda, Vladik Kreinovich.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXIII, 250 p. 8 illus., 4 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Big Data,
_x2197-6511 ;
_v115
505 0 _aIntroduction -- 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 _aThe 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 _aEngineering
_xData processing.
650 0 _aComputational intelligence.
650 0 _aBig data.
650 1 4 _aData Engineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aBig Data.
700 1 _aKreinovich, Vladik.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031167799
776 0 8 _iPrinted edition:
_z9783031167812
776 0 8 _iPrinted edition:
_z9783031167829
830 0 _aStudies in Big Data,
_x2197-6511 ;
_v115
856 4 0 _uhttps://doi.org/10.1007/978-3-031-16780-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c174023
_d174023