Data mining practical machine learning tools and techniques /
Saved in:
| 主要作者: | |
|---|---|
| 企业作者: | |
| 其他作者: | , |
| 格式: | 电子 电子书 |
| 语言: | 英语 |
| 出版: |
Amsterdam :
Elsevier/Morgan Kaufmann,
2011.
|
| 版: | 3rd ed. |
| 主题: | |
| 在线阅读: | An electronic book accessible through the World Wide Web; click to view |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
书本目录:
- 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.