机读格式显示(MARC)

• 000 01778cam a2200313 a 4500

• 001 0000401763

• 005 20121207085500.0

• 008 120416r20122011cc a b 001 0 eng d

• 020 __ |a 9787111374176 : |c CNY108.00

• 040 __ |a BUA |c BUA

• 050 _4 |a QA76.9.D343 |b W58 2011

• 082 04 |a 006.3/12 |2 22

• 090 __ |a TP311.13/WIH/(3)

• 093 __ |a TP311.13 |2 4

• 099 __ |a CAL 022012063127

• 100 1_ |a Witten, I. H. |q (Ian H.)

• 245 10 |a Data mining : |b practical machine learning tools and techniques = 数据挖掘 : 实用机器学习工具与技术 / |c Ian H. Witten, Eibe Frank, Mark A. Hall著.

• 246 31 |a 数据挖掘 : |b 实用机器学习工具与技术

• 250 __ |a 3rd ed.

• 260 __ |a Beijing : |b China Machine Press, |c 2012.

• 300 __ |a xxiv, 629 p. : |b ill. ; |c 24 cm.

• 490 0_ |a 经典原版书库

• 504 __ |a Includes bibliographical references (p. 587-605) and index.

• 505 0_ |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.

• 534 __ |p Reprint. Originally published: |c Burlington, MA : Morgan Kaufmann,c2011, |b 3th. |z 9780123748560.

• 650 _0 |a Data mining.

• 700 1_ |a Frank, Eibe.

• 700 1_ |a Hall, Mark A.

• 920 __ |a 211180 |z 2