| 000 | 01866cam a2200277zu 4500 | ||
|---|---|---|---|
| 001 | 88873222 | ||
| 003 | FRCYB88873222 | ||
| 005 | 20250107232240.0 | ||
| 006 | m o d | ||
| 007 | cr un | ||
| 008 | 250108s2019 fr | o|||||0|0|||eng d | ||
| 020 | _a9780128172162 | ||
| 035 | _aFRCYB88873222 | ||
| 040 |
_aFR-PaCSA _ben _c _erda |
||
| 100 | 1 | _aYang, Xin-She | |
| 245 | 0 | 1 |
_aIntroduction to Algorithms for Data Mining and Machine Learning _c['Yang, Xin-She'] |
| 264 | 1 |
_bElsevier Science _c2019 |
|
| 300 | _a p. | ||
| 336 |
_btxt _2rdacontent |
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| 337 |
_bc _2rdamdedia |
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| 338 |
_bc _2rdacarrier |
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| 650 | 0 | _a | |
| 700 | 0 | _aYang, Xin-She | |
| 856 | 4 | 0 |
_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88873222 _qtext/html _a |
| 520 | _aIntroduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-studyProvides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages | ||
| 999 |
_c72196 _d72196 |
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