| 000 | 02403nam a22003255i 4500 | ||
|---|---|---|---|
| 001 | 102989 | ||
| 003 | DE-He213 | ||
| 005 | 20240111050143.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 180118s2018 gw | s |||| 0|eng d | ||
| 020 | _a9783319730400 | ||
| 024 | 7 |
_a10.1007/978-3-319-73040-0 _2doi |
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| 050 | 4 | _aQ342 2018 EB | |
| 040 |
_aES-MaUEC _bspa |
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| 100 | 1 |
_aKovalerchuk, Boris _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n00001654 _1http://viaf.org/viaf/3699442/ _967488 |
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| 245 | 1 | 0 |
_aVisual Knowledge Discovery and Machine Learning _cby Boris Kovalerchuk. |
| 264 | 1 |
_aCham _bSpringer International Publishing _c2018 |
|
| 300 | _a1 recurso en línea (XXI, 317 páginas 274 ilustraciones, 263 ilustraciones a color) | ||
| 347 |
_atext file _bPDF |
||
| 490 | 0 |
_aIntelligent Systems Reference Library _x1868-4394 _v144 |
|
| 520 | 3 | _aThis book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science. | |
| 650 | 7 |
_aInteligencia artificial _2embne _9413115 |
|
| 776 | 0 | 8 |
_iEdición impresa: _z9783319730394 |
| 776 | 0 | 8 |
_iEdición impresa: _z9783319730417 |
| 776 | 0 | 8 |
_iEdición impresa: _z9783319892306 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-73040-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 490 | 0 | _aEngineering (Springer-11647) | |
| 988 | _aEBSPRINGER_2018 | ||
| 998 |
_b02/2019 _dz _ef _feng _ggw _h0 |
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| 999 |
_c102989 _d102989 _x1 |
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