000 | 01581cam a22002894a 4500 | ||
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001 | 16490354 | ||
003 | OSt | ||
005 | 20140227121553.0 | ||
008 | 101005s20132011ii a g b 001 0 eng | ||
010 | _a 2010039827 | ||
020 | _a9780123748560 (pbk.) | ||
020 | _a9789380501864 (pbk.) | ||
040 | _cNCL | ||
082 | 0 | 0 |
_a006.3 _bWIT-D 2013 4451 |
100 | 1 |
_aWitten, I. H. _q(Ian H.) |
|
245 | 1 |
_aData mining : _bpractical machine learning tools and techniques / _cby Ian H. Witten, Eibe Frank, Mark A. Hall. |
|
250 | _a3rd ed. | ||
260 |
_aNew Delhi : _bElsevier, _c2013. |
||
300 |
_av, 629 p. : _bill. ; _c24 cm. |
||
490 | 1 | _a[Morgan Kaufmann series in data management systems] | |
504 | _aIncludes bibliographical references (p. 587-605) and index. | ||
505 | 0 | _aPart I. Machine Learning Tools and Techniques: 1. What's iIt 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. | |
650 | 0 | _aData mining. | |
700 | 1 | _aFrank, Eibe. | |
700 | 1 | _aHall, Mark A. | |
942 |
_2ddc _cBK |
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999 |
_c3116 _d3116 |