|
|
.
|
|
|
0 (hodnocen0 x )
|
|
|
EB
|
|
|
ONLINE
|
|
|
|
|
|
|
|
|
3rd ed.
|
|
|
Amsterdam : Elsevier/Morgan Kaufmann, 2011
|
|
|
1 online resource (xxxiii, 629 p.) : ill
|
| Externí odkaz |
 Plný text PDF
|
|
* Návod pro vzdálený přístup
|
|
|
|
|
|
|
|
|
|
ISBN 9780080890364 (electronic bk.)
|
|
|
ISBN 0123748569 (pbk.)
|
|
|
ISBN 9780123748560 (pbk.)
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
|
|
001723591
|
|
|
full
|
|
|
(Au-PeEL)EBL634862
|
|
|
(CaONFJC)MIL295388
|
|
|
(CaPaEBR)ebr10525052
|
|
|
(MiAaPQ)EBC634862
|
|
|
(OCoLC)701704090
|
Dotazy a připomínky