Data mining practical machine learning tools and techniques /
Сохранить в:
| Главный автор: | |
|---|---|
| Соавтор: | |
| Другие авторы: | , |
| Формат: | Электронный ресурс eКнига |
| Язык: | английский |
| Опубликовано: |
Amsterdam :
Elsevier/Morgan Kaufmann,
2011.
|
| Редактирование: | 3rd ed. |
| Предметы: | |
| Online-ссылка: | An electronic book accessible through the World Wide Web; click to view |
| Метки: |
Добавить метку
Нет меток, Требуется 1-ая метка записи!
|
Оглавление:
- 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.