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Data mining :practical machine learning tools and techniques /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data mining :/ Ian H. Witten, Eibe Frank, Mark A. Hall.
其他題名:	practical machine learning tools and techniques /
作者:	Witten, I. H.
其他作者:	Frank, Eibe.
出版者:	Burlington, MA :Morgan Kaufmann, : c2011.,
版本:	3rd ed.
面頁冊數:	xxxiii, 629 p. :ill. ; : 24 cm.;
標題:	Data mining. -
ISBN:	9780123748560 (pbk.) :

Data mining :practical machine learning tools and techniques /
Witten, I. H.

Data mining :practical machine learning tools and techniques /Ian H. Witten, Eibe Frank, Mark A. Hall. - 3rd ed. - Burlington, MA :Morgan Kaufmann,c2011. - xxxiii, 629 p. :ill. ;24 cm. - [Morgan Kaufmann series in data management systems]. - Morgan Kaufmann series in data management systems..

Includes bibliographical references (p. 587-605) and index.

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.

ISBN: 9780123748560 (pbk.) :NT1947

LCCN: 2010039827Subjects--Topical Terms:

255525
Data mining.

LC Class. No.: QA76.9.D343 / W58 2011

Data mining :practical machine learning tools and techniques /
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250 $a 3rd ed.
260 $a Burlington, MA : $c c2011. $b Morgan Kaufmann,
300 $a xxxiii, 629 p. : $b ill. ; $c 24 cm.
490 1 $a [Morgan Kaufmann series in data management systems]
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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.
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700 1 $a Frank, Eibe. $3 320553
700 1 $a Hall, Mark A. $q (Mark Andrew) $3 320554
830 0 $a Morgan Kaufmann series in data management systems. $3 320555