| 000 | 02303nam a22003255i 4500 | ||
|---|---|---|---|
| 001 | 102136 | ||
| 003 | DE-He213 | ||
| 005 | 20240111050136.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 171221s2018 gw | s |||| 0|eng d | ||
| 020 | _a9783319716886 | ||
| 024 | 7 |
_a10.1007/978-3-319-71688-6 _2doi |
|
| 050 | 4 |
_aQ342 _b2018 EB |
|
| 100 | 1 |
_aGramacki, Artur _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 245 | 1 | 0 |
_aNonparametric Kernel Density Estimation and Its Computational Aspects _cby Artur Gramacki. |
| 264 | 1 |
_aCham _bSpringer International Publishing _c2018 |
|
| 300 | _a1 recurso en línea (XXIX, 176 páginas 70 ilustraciones) | ||
| 347 |
_atext file _bPDF |
||
| 490 | 0 |
_aStudies in Big Data _x2197-6503 _v37 |
|
| 520 | 3 | _aThis book describes computational problems related to kernel density estimation (KDE) - one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented. | |
| 650 | 7 |
_aInteligencia artificial _2embne _9413115 |
|
| 650 | 7 |
_aDatos masivos _9495511 |
|
| 776 | 0 | 8 |
_iEdición impresa: _z9783319716879 |
| 776 | 0 | 8 |
_iEdición impresa: _z9783319716893 |
| 776 | 0 | 8 |
_iEdición impresa: _z9783319890944 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-71688-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 490 | 0 | _aEngineering (Springer-11647) | |
| 988 | _aEBSPRINGER_2018 | ||
| 998 |
_b12/2018 _dz _ek _feng _ggw _h0 |
||
| 999 |
_c102136 _d102136 _x1 |
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