Data mining : practical machine learning tools and techniques / / Ian H. Witten, Eibe Frank, Mark A. Hall.
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| Year of Publication: | 2011 |
| Edition: | 3rd ed. |
| Language: | English |
| Online Access: | |
| Physical Description: | xxxiii, 629 p. :; ill. |
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(MiAaPQ)500634862 (Au-PeEL)EBL634862 (CaPaEBR)ebr10525052 (CaONFJC)MIL295388 (OCoLC)701704090 |
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Witten, I. H. (Ian H.) Data mining [electronic resource] : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. 3rd ed. Amsterdam : Elsevier/Morgan Kaufmann, 2011. xxxiii, 629 p. : ill. Includes bibliographical references and index. Part 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 -- 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. Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries. Data mining. Electronic books. Frank, Eibe. Hall, Mark A. ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=634862 Click to View |
| language |
English |
| format |
Electronic eBook |
| author |
Witten, I. H. |
| spellingShingle |
Witten, I. H. Data mining practical machine learning tools and techniques / Part 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 -- 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. |
| author_facet |
Witten, I. H. Frank, Eibe. Hall, Mark A. ProQuest (Firm) ProQuest (Firm) |
| author_variant |
i h w ih ihw |
| author_fuller |
(Ian H.) |
| author2 |
Frank, Eibe. Hall, Mark A. ProQuest (Firm) |
| author2_variant |
e f ef m a h ma mah |
| author2_role |
TeilnehmendeR TeilnehmendeR TeilnehmendeR |
| author_corporate |
ProQuest (Firm) |
| author_sort |
Witten, I. H. |
| title |
Data mining practical machine learning tools and techniques / |
| title_sub |
practical machine learning tools and techniques / |
| title_full |
Data mining [electronic resource] : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. |
| title_fullStr |
Data mining [electronic resource] : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. |
| title_full_unstemmed |
Data mining [electronic resource] : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. |
| title_auth |
Data mining practical machine learning tools and techniques / |
| title_new |
Data mining |
| title_sort |
data mining practical machine learning tools and techniques / |
| publisher |
Elsevier/Morgan Kaufmann, |
| publishDate |
2011 |
| physical |
xxxiii, 629 p. : ill. |
| edition |
3rd ed. |
| contents |
Part 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 -- 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 |
9780080890364 (electronic bk.) |
| callnumber-first |
Q - Science |
| callnumber-subject |
QA - Mathematics |
| callnumber-label |
QA76 |
| callnumber-sort |
QA 276.9 D343 W58 42011 |
| genre |
Electronic books. |
| genre_facet |
Electronic books. |
| url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=634862 |
| illustrated |
Illustrated |
| dewey-hundreds |
000 - Computer science, information & general works |
| dewey-tens |
000 - Computer science, knowledge & systems |
| dewey-ones |
006 - Special computer methods |
| dewey-full |
006.3/12 |
| dewey-sort |
16.3 212 |
| dewey-raw |
006.3/12 |
| dewey-search |
006.3/12 |
| oclc_num |
701704090 |
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