TY  - BOOK
AU  - Witten,I.H.
AU  - Frank,Eibe
AU  - Hall,Mark A.
ED  - ebrary, Inc.
TI  - Data mining: practical machine learning tools and techniques 
AV  - QA76.9.D343 W58 2011eb
U1  - 006.3/12 22
PY  - 2011///
CY  - Amsterdam
PB  - Elsevier/Morgan Kaufmann
KW  - Data mining
KW  - Electronic books
KW  - local
N1  - Includes bibliographical references and index; 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; Electronic reproduction; Palo Alto, Calif.; ebrary; 2012; Available via World Wide Web; Access may be limited to ebrary affiliated libraries
UR  - http://site.ebrary.com/lib/daystar/Doc?id=10525052
ER  -