000			02010nam a22003017a 4500
005			20230503104557.0
008			230503b ||||| |||| 00| 0 spa d
020			_a9780128042915
040			_aCO-ViULL
041			_heng
082			_223 _a006.312 _bW829d
100			_aWitten, Ian H. _9160772
245			_aData mining: _bpractical machine learning tools and techniques
250			_a4
260			_aUnited States : _bMorgan Kaufmann, _c2017
300			_a621 p. _b: fig. , tab.
500			_aContents.
505			_aChapter 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.
520			_aData 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
650			_aData mining _9160773
650			_aProcesamiento de datos _9160774
650			_aAssociation rule mining _9160775
650			_aData transformations _9160776
700			_aFrank, Eibe _9160777
700			_aHall, Mark A. _9160778
700			_aPal, Christopher J. _9160779
942			_2ddc _cBK
999			_c47468 _d47468