03329nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001700167072002300184072001600207245021200223250001800435264007300453300006500526336002600591337002600617338003600643347002400679490005600703505073600759520048901495650002601984650003502010650003102045650002702076650004002103650002402143650003502167650005102202650004002253650003202293650004402325700007702369700008302446710003402529773002602563776003602589776003602625776003602661830005602697856004602753978-3-031-66497-7DE-He21320251030113040.0cr nn 008mamaa240913s2024    sz |    s    |||| 0|eng d  a97830316649777 a10.1007/978-3-031-66497-72doi 4aQA221-224 7aPBKJ2bicssc 7aMAT0340002bisacsh 7aPBKJ2thema10aExplorations in the Mathematics of Data Scienceh[electronic resource] :bThe Inaugural Volume of the Center for Approximation and Mathematical Data Analytics /cedited by Simon Foucart, Stephan Wojtowytsch.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Birkhäuser,c2024.  aXIV, 286 p. 24 illus., 20 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aApplied and Numerical Harmonic Analysis,x2296-50170 aPreface -- S-Procedure Relaxation: a Case of Exactness Involving Chebyshev Centers -- Neural networks: deep, shallow, or in between? -- Qualitative neural network approximation over R and C -- Linearly Embedding Sparse Vectors from l2 to l1 via Deterministic Dimension-Reducing Maps -- Ridge Function Machines -- Learning Collective Behaviors from Observation -- Provably Accelerating Ill-Conditioned Low-Rank Estimation via Scaled Gradient Descent, Even with Overparameterization -- CLAIRE: Scalable GPU-Accelerated Algorithms for Diffeomorphic Image Registration in 3D -- A genomic tree based sparse solver -- A qualitative difference between gradient flows of convex functions in finite- and infinite-dimensional Hilbert spaces.  aThis edited volume reports on the recent activities of the new Center for Approximation and Mathematical Data Analytics (CAMDA) at Texas A&M University. Chapters are based on talks from CAMDA’s inaugural conference – held in May 2023 – and its seminar series, as well as work performed by members of the Center. They showcase the interdisciplinary nature of data science, emphasizing its mathematical and theoretical foundations, especially those rooted in approximation theory. 0aApproximation theory. 0aComputer sciencexMathematics. 0aInformation visualization. 0aQuantitative research. 0aData structures (Computer science). 0aInformation theory.14aApproximations and Expansions.24aMathematical Applications in Computer Science.24aData and Information Visualization.24aData Analysis and Big Data.24aData Structures and Information Theory.1 aFoucart, Simon.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aWojtowytsch, Stephan.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303166496008iPrinted edition:z978303166498408iPrinted edition:z9783031664991 0aApplied and Numerical Harmonic Analysis,x2296-501740uhttps://doi.org/10.1007/978-3-031-66497-7