Data mining practical machine learning tools and techniques /
I tiakina i:
| Kaituhi matua: | |
|---|---|
| Kaituhi rangatōpū: | |
| Ētahi atu kaituhi: | , |
| Hōputu: | Tāhiko īPukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
Amsterdam :
Elsevier/Morgan Kaufmann,
2011.
|
| Putanga: | 3rd ed. |
| Ngā marau: | |
| Urunga tuihono: | An electronic book accessible through the World Wide Web; click to view |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
Rārangi ihirangi:
- 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.