| 000 | 03728nam a22006135i 4500 | ||
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| 001 | 978-3-031-66497-7 | ||
| 003 | DE-He213 | ||
| 005 | 20251030113040.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 240913s2024 sz | s |||| 0|eng d | ||
| 020 |
_a9783031664977 _9978-3-031-66497-7 |
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| 024 | 7 |
_a10.1007/978-3-031-66497-7 _2doi |
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| 072 | 7 |
_aPBKJ _2bicssc |
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_aPBKJ _2thema |
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_aExplorations in the Mathematics of Data Science _h[electronic resource] : _bThe Inaugural Volume of the Center for Approximation and Mathematical Data Analytics / _cedited by Simon Foucart, Stephan Wojtowytsch. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aCham : _bSpringer Nature Switzerland : _bImprint: Birkhäuser, _c2024. |
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| 300 |
_aXIV, 286 p. 24 illus., 20 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aApplied and Numerical Harmonic Analysis, _x2296-5017 |
|
| 505 | 0 | _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. | |
| 520 | _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. | ||
| 650 | 0 |
_aApproximation theory. _974825 |
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| 650 | 0 |
_aComputer science _xMathematics. _964041 |
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| 650 | 0 |
_aInformation visualization. _964751 |
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| 650 | 0 | _aQuantitative research. | |
| 650 | 0 |
_aData structures (Computer science). _966708 |
|
| 650 | 0 |
_aInformation theory. _957783 |
|
| 650 | 1 | 4 |
_aApproximations and Expansions. _974827 |
| 650 | 2 | 4 |
_aMathematical Applications in Computer Science. _965518 |
| 650 | 2 | 4 |
_aData and Information Visualization. _966435 |
| 650 | 2 | 4 | _aData Analysis and Big Data. |
| 650 | 2 | 4 |
_aData Structures and Information Theory. _966709 |
| 700 | 1 |
_aFoucart, Simon. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _984209 |
|
| 700 | 1 |
_aWojtowytsch, Stephan. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _984210 |
|
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031664960 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031664984 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031664991 |
| 830 | 0 |
_aApplied and Numerical Harmonic Analysis, _x2296-5017 _984211 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-66497-7 |
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