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Data mining : practical machine learning tools and techniques

Author: I H Witten; Eibe Frank
Publisher: San Francisco, Calif. : Morgan Kaufmann ; Oxford : Elsevier Science [distributor], 2005.
Edition/Format:   Print book : English : 2nd edView all editions and formats
Summary:
This work offers a grounding in machine learning concepts combined with practical advice on applying machine learning tools and techniques in real-world data mining situations.
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: I H Witten; Eibe Frank
ISBN: 0120884070 9780120884070
OCLC Number: 68254621
Notes: Previous edition: 1999.
Description: xxxi, 525 pages
Contents: Pt. I. Machine learning tools and techniques --
1. What's it all about? --
2. Input : concepts, instances, and attributes --
3. Output : knowledge representation --
4. Algorithms : the basic methods --
5. Credibility : evaluating what's been learned --
6. Implementations : real machine learning schemes --
7. Transformations : engineering the input and output --
8. Moving on : extensions and applications --
Pt. II. The Weka machine learning workbench --
9. Introduction to Weka --
10. The Explorer --
11. The knowledge flow interface --
12. The experimenter --
13. The command-line interface --
14. Embedded machine learning --
15. Writing new learning schemes.
Responsibility: Ian H. Witton and Eibe Frank.
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As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. This book includes comprehensive information on neural networks. It  Read more...

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"I was a big fan of the first edition and I'm excited about this new edition. - Peter Norvig, Director of Search Quality, Google, Inc."This book presents this new discipline in a very accessible Read more...

 
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