Data mining practical machine learning tools and techniques
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaime...
| Otros Autores: | , , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Cambridge, MA :
Morgan Kaufmann Publisher
[2017]
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| Edición: | 4th ed |
| Colección: | Science Direct e-books.
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| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b41091188*spi |
Tabla de Contenidos:
- 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.
