LEADER 03179nam 2200625 a 450 001 9910785305703321 005 20200520144314.0 010 $a1-282-95388-5 010 $a9786612953880 010 $a0-08-089036-9 035 $a(CKB)2670000000062246 035 $a(EBL)634862 035 $a(OCoLC)701704090 035 $a(SSID)ssj0000468262 035 $a(PQKBManifestationID)11335202 035 $a(PQKBTitleCode)TC0000468262 035 $a(PQKBWorkID)10498313 035 $a(PQKB)10223535 035 $a(Au-PeEL)EBL634862 035 $a(CaPaEBR)ebr10525052 035 $a(CaONFJC)MIL295388 035 $a(CaSebORM)9780123748560 035 $a(MiAaPQ)EBC634862 035 $a(PPN)170264319 035 $a(EXLCZ)992670000000062246 100 $a20101005d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData mining$b[electronic resource] $epractical machine learning tools and techniques /$fIan H. Witten, Eibe Frank, Mark A. Hall 205 $a3rd ed. 210 $aAmsterdam $cElsevier/Morgan Kaufmann$d2011 215 $a1 online resource (665 p.) $cill 225 1 $aThe Morgan Kaufmann Series in Data Management Systems 300 $aDescription based upon print version of record 311 $a0-12-374856-9 320 $aIncludes bibliographical references and index. 327 $aPart 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. 330 $aData Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizatio 410 4$aThe Morgan Kaufmann Series in Data Management Systems 606 $aData mining 615 0$aData mining. 676 $a006.3/12 700 $aWitten$b I. H$g(Ian H.)$028571 701 $aFrank$b Eibe$028572 701 $aHall$b Mark A$01467941 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910785305703321 996 $aData mining$93678839 997 $aUNINA