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|a 9780128042915
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| 040 |
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|a WAA
|c WAA
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|a spa
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| 100 |
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|a Witten, Ian H.
|9 5640
|
| 245 |
1 |
0 |
|a Data mining :
|b practical machine learning tools and techniques /
|c Ian H. Witten... [et al.]
|
| 250 |
|
|
|a 4th. ed.
|
| 260 |
3 |
0 |
|a Boston (Mass.) :
|b Elsevier,
|c 2017
|
| 300 |
|
|
|a 621 p.
|
| 505 |
|
|
|a Part I: Introduction to data mining. Chapter 1. What’s it all about? -- Chapter 2. Input: Concepts, instances, attributes -- Chapter 3. Output: Knowledge representation -- Chapter 4. Algorithms: The basic methods -- Chapter 5. Credibility: Evaluating what’s been learned -- Part II: More advanced machine learning schemes. Chapter 6. Trees and rules -- Chapter 7. Extending instance-based and linear models -- Chapter 8. Data transformations -- Chapter 9. Probabilistic methods -- Chapter 10. Deep learning -- Chapter 11. Beyond supervised and unsupervised learning -- Chapter 12 - Ensemble learning -- Chapter 13 - Moving on: applications and beyond -- Appendix A -- Theoretical foundations -- Appendix B - The WEKA workbench.
|
| 650 |
|
4 |
|6 Informática
|9 13
|
| 650 |
|
4 |
|a Inteligencia artificial
|9 2890
|
| 650 |
|
4 |
|9 5646
|a Aprendizaje automático
|
| 650 |
|
4 |
|9 5647
|a Minería de datos
|
| 700 |
1 |
|
|a Frank, Eibe
|9 5641
|
| 700 |
1 |
|
|a Hall, Mark A.
|9 5642
|
| 700 |
1 |
|
|a Pal, Christopher J.
|9 5643
|
| 942 |
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|c LIBRO
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|y LIBRO NPP
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