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230201s2011 xxua r 000 0 eng d |
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|a 9780123748560
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| 024 |
8 |
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|a DIF-M5751
|b 5859
|z DIF005367
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| 040 |
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|a AR-LpUFIB
|b spa
|c AR-LpUFIB
|
| 100 |
1 |
|
|a Witten, Ian H.
|
| 245 |
1 |
0 |
|a Data mining :
|b practical machine learning tools and techniques
|
| 250 |
|
|
|a 3rd ed.
|
| 260 |
|
|
|a Amsterdam :
|b Morgan Kaufmann,
|c 2011
|
| 300 |
|
|
|a xxxiii, 629 p. :
|b il. ;
|c 24 cm.
|
| 500 |
|
|
|a Incluye índice y bibliografía
|
| 505 |
0 |
|
|a Part I. Machine Learning Tools and Techniques: -- 1. Whats iIt all about? -- 2. Input: concepts, instances, and attributes -- 3. Output: knowledge representation -- 4. Algorithms: the basic methods -- 5. Credibility: evaluating whats been learned -- Part II. Advanced Data Mining: -- 6. Implementations: real machine learning schemes -- 7. Data transformation -- 8. Ensemble learning -- 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: -- 10. Introduction to Weka -- 11. The explorer -- 12. The knowledge flow interface -- 13. The experimenter -- 14 The command-line interface -- 15. Embedded machine learning -- 16. Writing new learning schemes -- 17. Tutorial exercises for the weka explorer.
|
| 650 |
|
4 |
|a MINERÍA DE DATOS
|
| 653 |
|
|
|a aprendizaje automático
|
| 700 |
1 |
|
|a Frank, Eibe ,
|e Autor
|
| 700 |
1 |
|
|a Hall, Mark A. ,
|e Autor
|
| 700 |
1 |
|
|a Autor,
|e Autor
|
| 942 |
|
|
|c BK
|
| 952 |
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|i DIF-03870
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|p DIF-03870
|r 2025-03-11 17:03:59
|w 2025-03-11
|y BK
|
| 999 |
|
|
|c 55156
|d 55156
|
