LEADER 00838nam0-22003011i-450- 001 990003839510403321 010 $a0-412-73060-X 035 $a000383951 035 $aFED01000383951 035 $a(Aleph)000383951FED01 035 $a000383951 100 $a20011116d1997----km-y0itay50------ba 101 0 $aeng 200 1 $aComputer-aided multivariate analysis$fA.A. Afifi and V. Clark 205 $a3rd ed 210 $aBoca Raton$cChapman & Hall$d1997 215 $a455 p.$d24 cm 225 1 $aText in statistical science 676 $a519 700 1$aAfifi,$bAbdelmonem A.$014428 701 1$aClark,$bV.$0382289 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003839510403321 952 $aXI-A-72$b8626$fMAS 959 $aMAS 996 $aComputer-aided multivariate analysis$9506970 997 $aUNINA LEADER 00839nam0-22003011i-450- 001 990006048450403321 005 19980601 035 $a000604845 035 $aFED01000604845 035 $a(Aleph)000604845FED01 035 $a000604845 100 $a19980601d1979----km-y0itay50------ba 105 $a--------00-yy 200 1 $a<>fasti$ftesto latino e traduzione in versi italiani di Ferruccio Bernini. 210 $aBologna$cZanichelli$d1979 215 $aXV,, 350 p.$d19 cm 225 1 $aPoeti di Roma$v11 676 $a871 700 1$aOvidius Naso,$bPublius$f<43 a. C.-17?>$0154954 702 1$aBernini,$bFerruccio 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006048450403321 952 $aIV ZZ PO (11)$b2333*$fFGBC 959 $aFGBC 996 $aFasti$912614 997 $aUNINA DB $aGIU01 LEADER 03762nam 2200577Ia 450 001 9910968813503321 005 20250717111917.0 010 $a9786611008062 010 $a0-08-047702-X 010 $a9781423722442 010 $a1-281-00806-0 035 $a(Au-PeEL)EBL234978 035 $a(CaPaEBR)ebr10127947 035 $a(CaONFJC)MIL100806 035 $a(OCoLC)936903533 035 $a(CaSebORM)9780120884070 035 $a(MiAaPQ)EBC234978 035 $a(PPN)191249068 035 $a(CKB)1000000000214589 035 $a(EXLCZ)991000000000214589 100 $a20050303d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData mining $epractical machine learning tools and techniques /$fIan H. Witten, Eibe Frank 205 $a2nd ed. 210 $aAmsterdam ;$aBoston, MA $cMorgan Kaufman$d2005 215 $a1 online resource (xxxi, 524 p.) $cill 225 1 $aMorgan Kaufmann series in data management systems 311 08$a0-12-088407-0 311 08$a1-4237-2244-2 320 $aIncludes bibliographical references (p. 485-503) 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; 6 Implementations: Real machine learning schemes; 7 Transformations: Engineering the input and output; 8 Moving on: Extensions and applications; PART 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; References; Index; About the Authors. 330 $aAs with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. Algorithmic methods at the heart of successful data mining including tried and true techniques as well as leading edge methods. Performance improvement techniques that work by transforming the input or output. Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization in a new, interactive interface. 410 0$aMorgan Kaufmann series in data management systems. 606 $aData mining 606 $aDatabase searching 615 0$aData mining. 615 0$aDatabase searching. 676 $a006.3 700 $aWitten$b I. H$g(Ian H.)$028571 701 $aFrank$b Eibe$028572 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910968813503321 996 $aData mining$9374289 997 $aUNINA