LEADER 01058cam0 2200277 450 001 E600200027372 005 20210525080124.0 100 $a20070601d2004 |||||ita|0103 ba 101 $aita 102 $aIT 200 1 $aCentocelle I$eRoma S.D.O. le indagini archeologiche$fcur. Patrizia Gioia$gRita Volpe$gtesti di Arnoldus Huyzendveld [et al] 210 $aSoveria Mannelli$cRubbettino$d2004 215 $a479 p., c.ripieg.$cill.$d30 cm 225 2 $aStudi e materiali dei Musei e Monumenti comunali di Roma 410 1$1001LAEC00023622$12001 $a*Studi e materiali dei Musei e Monumenti comunali di Roma 702 1$aVolpe, Rita$3A600200041950$4070 702 1$aGioia, Patrizia$3A600200042069$4070 801 0$aIT$bUNISOB$c20210525$gRICA 850 $aUNISOB 852 $aUNISOB$j930$m134957 912 $aE600200027372 940 $aM 102 Monografia moderna SBN 941 $aM 957 $a930$b000427$gSi$d134957$racquisto$1pomicino$2UNISOB$3UNISOB$420070601102653.0$520210525080112.0$6Alfano 996 $aCentocelle I$91686152 997 $aUNISOB LEADER 03233nam 2200709Ia 450 001 9910139896403321 005 20200520144314.0 010 $a9786612345661 010 $a9781118211038 010 $a1118211030 010 $a9781282345669 010 $a1282345664 010 $a9780470503065 010 $a0470503068 010 $a9780470503041 010 $a0470503041 035 $a(CKB)1000000000808213 035 $a(EBL)469484 035 $a(OCoLC)476311758 035 $a(SSID)ssj0000301138 035 $a(PQKBManifestationID)11265136 035 $a(PQKBTitleCode)TC0000301138 035 $a(PQKBWorkID)10260284 035 $a(PQKB)11010483 035 $a(MiAaPQ)EBC469484 035 $a(Perlego)1006513 035 $a(EXLCZ)991000000000808213 100 $a20090323d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aKnowledge discovery with support vector machines /$fLutz Hamel 210 $aHoboken, NJ $cJohn Wiley & Sons$d2009 215 $a1 online resource (266 p.) 225 1 $aWiley series on methods and applications in data mining 300 $aDescription based upon print version of record. 311 08$a9780470371923 311 08$a0470371927 320 $aIncludes bibliographical references and index. 327 $aKNOWLEDGE DISCOVERY WITH SUPPORT VECTOR MACHINES; CONTENTS; PREFACE; PART I; 1 WHAT IS KNOWLEDGE DISCOVERY?; 2 KNOWLEDGE DISCOVERY ENVIRONMENTS; 3 DESCRIBING DATA MATHEMATICALLY; 4 LINEAR DECISION SURFACES AND FUNCTIONS; 5 PERCEPTRON LEARNING; 6 MAXIMUM-MARGIN CLASSIFIERS; PART II; 7 SUPPORT VECTOR MACHINES; 8 IMPLEMENTATION; 9 EVALUATING WHAT HAS BEEN LEARNED; 10 ELEMENTS OF STATISTICAL LEARNING THEORY; PART III; 11 MULTICLASS CLASSIFICATION; 12 REGRESSION WITH SUPPORT VECTOR MACHINES; 13 NOVELTY DETECTION; APPENDIX A NOTATION; APPENDIX B TUTORIAL INTRODUCTION TO R 327 $aB.1 Programming ConstructsB.2 Data Constructs; B.3 Basic Data Analysis; Bibliographic Notes; REFERENCES; INDEX 330 $aAn easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi- 410 0$aWiley series on methods and applications in data mining. 606 $aSupport vector machines 606 $aData mining 606 $aMachine learning 606 $aComputer algorithms 615 0$aSupport vector machines. 615 0$aData mining. 615 0$aMachine learning. 615 0$aComputer algorithms. 676 $a005.1 700 $aHamel$b Lutz$0521577 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139896403321 996 $aKnowledge discovery with support vector machines$9837271 997 $aUNINA