Data mining practical machine learning tools and techniques /
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| Main Author: | |
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| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Amsterdam :
Elsevier/Morgan Kaufmann,
2011.
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| Edition: | 3rd ed. |
| Subjects: | |
| Online Access: | An electronic book accessible through the World Wide Web; click to view |
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Table of 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 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.