Data mining practical machine learning tools and techniques /

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Dades bibliogràfiques
Autor principal: Witten, I. H. (Ian H.)
Autor corporatiu: ebrary, Inc
Altres autors: Frank, Eibe, Hall, Mark A.
Format: Electrònic eBook
Idioma:anglès
Publicat: Amsterdam : Elsevier/Morgan Kaufmann, 2011.
Edició:3rd ed.
Matèries:
Accés en línia:An electronic book accessible through the World Wide Web; click to view
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Taula de continguts:
  • 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 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.