| 000 | 03285nam a22005175i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-73040-0 | ||
| 003 | DE-He213 | ||
| 005 | 20220801221416.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 180118s2018 sz | s |||| 0|eng d | ||
| 020 |
_a9783319730400 _9978-3-319-73040-0 |
||
| 024 | 7 |
_a10.1007/978-3-319-73040-0 _2doi |
|
| 050 | 4 | _aQ342 | |
| 072 | 7 |
_aUYQ _2bicssc |
|
| 072 | 7 |
_aTEC009000 _2bisacsh |
|
| 072 | 7 |
_aUYQ _2thema |
|
| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aKovalerchuk, Boris. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _955860 |
|
| 245 | 1 | 0 |
_aVisual Knowledge Discovery and Machine Learning _h[electronic resource] / _cby Boris Kovalerchuk. |
| 250 | _a1st ed. 2018. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
|
| 300 |
_aXXI, 317 p. 274 illus., 263 illus. in color. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aIntelligent Systems Reference Library, _x1868-4408 ; _v144 |
|
| 505 | 0 | _aMotivation, Problems and Approach -- General Line Coordinates (GLC) -- Theoretical and Mathematical Basis of GLC -- Adjustable GLCs for decreasing occlusion and pattern simplification -- GLC Case Studies -- Discovering visual features and shape perception capabilities in GLC -- Interactive Visual Classification, Clustering and Dimension Reduction with GLC-L -- Knowledge Discovery and Machine Learning for Investment Strategy with CPC. | |
| 520 | _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 | 0 |
_aComputational intelligence. _97716 |
|
| 650 | 0 |
_aArtificial intelligence. _93407 |
|
| 650 | 1 | 4 |
_aComputational Intelligence. _97716 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
| 710 | 2 |
_aSpringerLink (Online service) _955861 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319730394 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319730417 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319892306 |
| 830 | 0 |
_aIntelligent Systems Reference Library, _x1868-4408 ; _v144 _955862 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-73040-0 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c79637 _d79637 |
||