LEADER 00975cam0-22003131i-450- 001 990006268010403321 005 19980601 035 $a000626801 035 $aFED01000626801 035 $a(Aleph)000626801FED01 035 $a000626801 100 $a19980601d1996----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay---n---001yy 200 1 $a<>codice penitenziario e della sorveglianza con formulario$eillustrato con il commento, la giurisprudenza e le circolari$f[a cura di] Giancarlo Zappa, Cesare Massetti 210 $aPiacenza$cLa tribuna$dc1996 215 $a1534 p.$d20 cm 676 $a345.05$v20$zita 702 1$aMassetti,$bCesare 702 1$aZappa,$bGiancarlo 710 01$aItalia$0423419 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006268010403321 952 $aXIII Z 119$b30271*$fFGBC 959 $aFGBC 996 $aCodice penitenziario e della sorveglianza con formulario$9640535 997 $aUNINA LEADER 02804nam 2200469 450 001 9910495190603321 005 20220421130439.0 010 $a981-16-1446-6 024 7 $a10.1007/978-981-16-1446-0 035 $a(CKB)4100000011995246 035 $a(DE-He213)978-981-16-1446-0 035 $a(MiAaPQ)EBC6690581 035 $a(Au-PeEL)EBL6690581 035 $a(OCoLC)1263870828 035 $a(PPN)257355626 035 $a(EXLCZ)994100000011995246 100 $a20220421d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSparse estimation with math and R $e100 exercises for building logic /$fJoe Suzuki 205 $a1st ed. 2021. 210 1$aSingapore :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (X, 234 p. 54 illus., 46 illus. in color.) 311 $a981-16-1445-8 320 $aIncludes bibliographical references. 327 $aChapter 1: Linear Regression -- Chapter 2: Generalized Linear Regression -- Chapter 3: Group Lasso -- Chapter 4: Fused Lasso -- Chapter 5: Graphical Model -- Chapter 6: Matrix Decomposition -- Chapter 7: Multivariate Analysis. 330 $aThe most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building R programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers? insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis. 606 $aMultivariate analysis 615 0$aMultivariate analysis. 676 $a519.535 700 $aSuzuki$b Joe$0846228 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910495190603321 996 $aSparse Estimation with Math and R$91896754 997 $aUNINA