Data mining : practical machine learning tools and techniques /
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| Další autoři: | , |
| Médium: | Sách giấy |
| Vydáno: |
Burlington, MA :
Morgan Kaufmann,
c2011.
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| Vydání: | 3rd ed. |
| Edice: | Morgan Kaufmann series in data management systems.
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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Obsah:
- 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.