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| LEADER |
01901nam a2200409 4500 |
| 001 |
DLU110129141 |
| 005 |
##20111216 |
| 008 |
##101005s2011 maua b 001 0 eng |
| 010 |
# |
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|a 2010039827
|
| 020 |
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|a 0123748569 (pbk.)
|
| 020 |
# |
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|a 9780123748560 (pbk.)
|
| 035 |
# |
# |
|a (OCoLC)ocn262433473
|
| 040 |
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|a DLC
|c DLC
|d YDX
|d BTCTA
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|d DEBSZ
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|d IUL
|d DLC
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| 042 |
# |
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|a pcc
|
| 082 |
# |
# |
|a 006.3
|b WI-I
|
| 100 |
# |
# |
|a Witten, I. H.
|q (Ian H.)
|
| 245 |
# |
# |
|a Data mining :
|b practical machine learning tools and techniques /
|c Ian H. Witten, Eibe Frank, Mark A. Hall.
|
| 250 |
# |
# |
|a 3rd ed.
|
| 260 |
# |
# |
|a Burlington, MA :
|b Morgan Kaufmann,
|c c2011.
|
| 300 |
# |
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|a xxxiii, 629 p. :
|b ill. ;
|c 24 cm.
|
| 504 |
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|a Includes bibliographical references (p. 587-605) and index.
|
| 505 |
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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.
|
| 650 |
# |
# |
|a Data mining.
|
| 700 |
# |
# |
|a Frank, Eibe.
|
| 700 |
# |
# |
|a Hall, Mark A.
|
| 830 |
# |
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|a Morgan Kaufmann series in data management systems.
|
| 923 |
# |
# |
|a 19/2011
|
| 991 |
# |
# |
|a GT
|
| 992 |
# |
# |
|a 1667757
|
| 994 |
# |
# |
|a DLU
|
| 900 |
# |
# |
|a True
|
| 911 |
# |
# |
|a Đào Thị Thu Huyền
|
| 925 |
# |
# |
|a G
|
| 926 |
# |
# |
|a A
|
| 927 |
# |
# |
|a SH
|
| 980 |
# |
# |
|a Thư viện Trường Đại học Đà Lạt
|
