000			02198nam a22003497i 4500
005			20251030135902.0
007			t|
008			230503s2017 ||||| |||| 00| 0 spa d
020			_a9780128042915
040			_aCO-ViULL _erda _bspa
041			_heng
082	0		_223 _a006.312 _bW829d
100	1		_aWitten, Ian H. _9160772, _eautor. _4aut
245	1	0	_aData mining : _bpractical machine learning tools and techniques.
250			_a4.
264		1	_aUnited States : _bMorgan Kaufmann, _c2017.
300			_a621 páginas : _b: figuras , tables.
336			_atexto _btxt _2rdacontent
337			_asin mediación _bn _2rdamedia
338			_avolumen _bnc _2rdacarrier
500			_aContents.
505	8		_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		0	_aData mining _9160773.
650		0	_aProcesamiento de datos _9160774.
650		0	_aAssociation rule mining _9160775.
650		0	_aData transformations _9160776.
700	1		_aFrank, Eibe _9160777.
700	1		_aHall, Mark A. _9160778.
700	1		_aPal, Christopher J. _9160779.
942			_2ddc _cBK
999			_c47468 _d47468