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- 2010039827 contributor B11784399.
- 2010039827 contributor B11784400.
- 2010039827 created "c2011.".
- 2010039827 date "2011".
- 2010039827 date "c2011.".
- 2010039827 dateCopyrighted "c2011.".
- 2010039827 description "Includes bibliographical references (p. 587-605) and index.".
- 2010039827 description "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.".
- 2010039827 extent "xxxiii, 629 p. :".
- 2010039827 identifier "0123748569 (pbk.)".
- 2010039827 identifier "9780123748560 (pbk.)".
- 2010039827 isPartOf "Morgan Kaufmann series in data management systems.".
- 2010039827 isPartOf "[Morgan Kaufmann series in data management systems]".
- 2010039827 issued "2011".
- 2010039827 issued "c2011.".
- 2010039827 language "eng".
- 2010039827 publisher "Burlington, MA : Morgan Kaufmann,".
- 2010039827 subject "006.3/12 22".
- 2010039827 subject "Data mining.".
- 2010039827 subject "QA76.9.D343 W58 2011".
- 2010039827 tableOfContents "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.".
- 2010039827 title "Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall.".
- 2010039827 type "text".