| 000 | 01728nam a2200301 a 4500 | ||
|---|---|---|---|
| 001 | vtls000056575 | ||
| 003 | KUKTEM | ||
| 005 | 20251125093043.0 | ||
| 008 | 111205t2011 maua f 001 0 eng d | ||
| 020 | _a9780123748560 (pbk.) | ||
| 020 | _a0123748569 (pbk.) | ||
| 039 | 9 |
_a201204021030 _basmadi _y201112051102 _zsri |
|
| 040 | _aUMP | ||
| 090 | _aQA76.9.D343 W58 2011 | ||
| 100 | 1 | _aWitten, Ian H. | |
| 245 | 1 | 0 |
_aData mining : _bpractical machine learning tools and techniques / _cIan H. Witten, Eibe Frank, Mark A. Hall |
| 250 | _a3rd ed. | ||
| 260 |
_aBurlington, MA. : _bMorgan Kaufmann, _c2011 |
||
| 300 |
_axxxiii, 629 p. : _bill. ; _c24 cm. |
||
| 490 | 0 | _aMorgan 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. | |
| 999 |
_aVIRTUA40 _c60930 _d60936 |
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| 999 | _aVTLSSORT0080*0200*0201*0400*0900*1000*2450*2500*2600*3000*4900*5040*5050*6500*7000*7001*9991 | ||
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