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    Witten, I. H.; Frank, Eibe; Hall, Mark A.
    Part I. Machine Learning Tools and Techniques: -- 1. What's it 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 Mining Workbench: -- 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.
    Digital Access  ScienceDirect 2011