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  • Book
    Witten, I. H.; Frank, Eibe; Hall, Mark A.
    Contents:
    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