Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall
Material type:
TextSeries: [Morgan Kaufmann series in data management systems]Publication details: Burlington, MA Morgan Kaufmann cop. 2011Edition: 3. edDescription: xxxiii, 629 p. ill. 24 cmISBN: - 9780123748560 (pbk.)
- 006.312 22
- 006.3
- Pud
| Item type | Current library | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Book (Same day loan) | Campus Karlskrona Referens | 006.3 | Available | 080041486716 | |
| Book (loan) | Campus Karlskrona | 006.3 | Available | 080041486715 |
Includes bibliographical references and index
PART I: Machine Learning Tools and Techniques. Ch 1. What's It All About? Ch 2. Input: Concepts, Instances, Attributes. Ch 3. Output: Knowledge Representation. Ch 4. Algorithms: The Basic Methods. Ch 5. Credibility: Evaluating What's Been Learned. PART II: Advanced Data Mining.Ch 6. Implementations: Real Machine Learning Schemes. Ch 7. Data Transformation. Ch 8. Ensemble Learning. Ch 9. Moving On: Applications and Beyond. PART III: The Weka Data MiningWorkbench. Ch 10. Introduction to Weka. Ch 11. The Explorer. Ch 12. The Knowledge Flow Interface. Ch 13. The Experimenter. Ch 14 The Command-Line Interface. Ch 15. Embedded Machine Learning. Ch 16. Writing New Learning Schemes. Ch 17. Tutorial Exercises for the Weka Explorer