practical machine learning tools and techniques Witten, I. H. (Ian H.) creator Frank, Eibe. Hall, Mark A. text bibliography mau Burlington, MA Morgan Kaufmann c2011 2011 3rd ed. monographic eng

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xxxiii, 629 p. : ill. ; 24 cm. Part I. Machine Learning Tools and Techniques: 1. What's iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer. Ian H. Witten, Eibe Frank, Mark A. Hall. Includes bibliographical references (p. 587-605) and index. Data mining QA76.9.D343 W58 2011 006.312 WID 9780123748560 (pbk.) 0123748569 (pbk.) 2010039827 DLC 101005 20150408114159.0 16490354