LEADER 03701nam 22006375 450 001 9910303450903321 005 20200629143909.0 010 $a3-030-05228-1 024 7 $a10.1007/978-3-030-05228-7 035 $a(CKB)4100000007223472 035 $a(DE-He213)978-3-030-05228-7 035 $a(MiAaPQ)EBC6312502 035 $a(PPN)232964327 035 $a(EXLCZ)994100000007223472 100 $a20181214d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApproximation Theory and Algorithms for Data Analysis /$fby Armin Iske 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (X, 358 p. 34 illus., 15 illus. in color.) 225 1 $aTexts in Applied Mathematics,$x0939-2475 ;$v68 311 $a3-030-05227-3 320 $aIncludes bibliographical references and index. 327 $a1 Introduction -- 2 Basic Methods and Numerical Analysis -- 3 Best Approximations -- 4 Euclidean Approximations -- 5 Chebyshev Approximations -- 6 Asymptotic Results -- 7 Basic Concepts of Signal Approximation -- 8 Kernel-Based Approximation -- 9 Computational Topology -- References -- Subject Index -- Name Index. 330 $aThis textbook offers an accessible introduction to the theory and numerics of approximation methods, combining classical topics of approximation with recent advances in mathematical signal processing, and adopting a constructive approach, in which the development of numerical algorithms for data analysis plays an important role. The following topics are covered: * least-squares approximation and regularization methods * interpolation by algebraic and trigonometric polynomials * basic results on best approximations * Euclidean approximation * Chebyshev approximation * asymptotic concepts: error estimates and convergence rates * signal approximation by Fourier and wavelet methods * kernel-based multivariate approximation * approximation methods in computerized tomography Providing numerous supporting examples, graphical illustrations, and carefully selected exercises, this textbook is suitable for introductory courses, seminars, and distance learning programs on approximation for undergraduate students. 410 0$aTexts in Applied Mathematics,$x0939-2475 ;$v68 606 $aApproximation theory 606 $aComputer science$xMathematics 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aApproximations and Expansions$3https://scigraph.springernature.com/ontologies/product-market-codes/M12023 606 $aComputational Mathematics and Numerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M1400X 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 615 0$aApproximation theory. 615 0$aComputer science$xMathematics. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 14$aApproximations and Expansions. 615 24$aComputational Mathematics and Numerical Analysis. 615 24$aSignal, Image and Speech Processing. 676 $a511.4 676 $a511.4 700 $aIske$b Armin$4aut$4http://id.loc.gov/vocabulary/relators/aut$0768209 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910303450903321 996 $aApproximation Theory and Algorithms for Data Analysis$91564660 997 $aUNINA