02198nam a22003497i 4500 20251030135902.0 t| 230503s2017 ||||| |||| 00| 0 spa d 9780128042915 CO-ViULL rda spa eng 23 006.312 W829d Witten, Ian H. 160772, autor. aut Data mining : practical machine learning tools and techniques. 4. United States : Morgan Kaufmann, 2017. 621 páginas : : figuras , tables. texto txt rdacontent sin mediación n rdamedia volumen nc rdacarrier Contents. 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. -- 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. -- 10.5 Stochastic Deep Networks. -- Chapter 11. Beyond supervised and unsupervised learning. -- Chapter 12. Ensemble learning. -- Chapter 13. Moving on: applications and beyond. 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 acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Data mining 160773. Procesamiento de datos 160774. Association rule mining 160775. Data transformations 160776. Frank, Eibe 160777. Hall, Mark A. 160778. Pal, Christopher J. 160779. ddc BK 47468 47468 0 0 ddc 0 0 001 001 GEN 2023-05-03 El bibliotecólogo Compra 339.15 0000023389 0 006.312 W829d 086922 2024-05-15 00:00:00 Ej.:1 2023-05-03 ISF BK