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01962cam a22003254a 4500 |
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20140107143158.0 |
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101005s2011 maua b 001 0 eng |
| 010 |
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|a 2010039827
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| 020 |
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|a 9780123748560 (pbk.)
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| 020 |
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|a 0123748569 (pbk.)
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|a DLC
|c DLC
|d YDX
|d BTCTA
|d YDXCP
|d BWX
|d DEBSZ
|d CDX
|d IUL
|d HR-ZaFER
|b hrv
|e ppiak
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| 042 |
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|a pcc
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| 050 |
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|a QA76.9.D343
|b W58 2011
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| 082 |
0 |
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|a 006.3/12
|2 22
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| 100 |
1 |
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|a Witten, I. H.
|q (Ian H.)
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| 245 |
1 |
0 |
|a Data mining :
|b practical machine learning tools and techniques /
|c Ian H. Witten, Eibe Frank, Mark A. Hall.
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| 250 |
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|a 3rd ed.
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| 260 |
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|a Burlington, MA :
|b Morgan Kaufmann,
|c c2011.
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| 300 |
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|a xxxiii, 629 str. :
|b ilustr. ;
|c 24 cm.
|
| 490 |
1 |
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|a [Morgan Kaufmann series in data management systems]
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| 504 |
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|a Includes bibliographical references (p. 587-605) and index.
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| 505 |
0 |
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|a Part 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.
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| 650 |
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0 |
|a Data mining.
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| 700 |
1 |
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|a Frank, Eibe.
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| 700 |
1 |
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|a Hall, Mark A.
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| 830 |
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0 |
|a Morgan Kaufmann series in data management systems.
|
| 906 |
|
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|a 7
|b cbc
|c orignew
|d 1
|e ecip
|f 20
|g y-gencatlg
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| 942 |
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|2 udc
|c K
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| 955 |
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|b xh00 2010-10-05
|a xh07 2010-10-05 ONIX awaiting for author/edition info.
|i xh07 2010-10-08 to Dewey
|a xe14 2011-04-20 2 copies rec'd., to CIP ver.
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| 999 |
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|c 42944
|d 42944
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