LEADER 02840nam 2200469 450 001 996464436203316 005 20220723142158.0 010 $a981-16-1438-5 035 $a(CKB)5470000001298887 035 $a(MiAaPQ)EBC6796475 035 $a(Au-PeEL)EBL6796475 035 $a(OCoLC)1282008216 035 $a(PPN)258301066 035 $a(EXLCZ)995470000001298887 100 $a20220723d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSparse estimation with math and Python $e100 exercises for building logic /$fJoe Suzuki 210 1$aGateway East, Singapore :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (254 pages) 311 $a981-16-1437-7 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- What makes SEMP unique? -- Contents -- 1 Linear Regression -- 1.1 Linear Regression -- 1.2 Subderivative -- 1.3 Lasso -- 1.4 Ridge -- 1.5 A Comparison Between Lasso and Ridge -- 1.6 Elastic Net -- 1.7 About How to Set the Value of ? -- 2 Generalized Linear Regression -- 2.1 Generalization of Lasso in Linear Regression -- 2.2 Logistic Regression for Binary Values -- 2.3 Logistic Regression for Multiple Values -- 2.4 Poisson Regression -- 2.5 Survival Analysis -- 3 Group Lasso -- 3.1 When One Group Exists -- 3.2 Proxy Gradient Method -- 3.3 Group Lasso -- 3.4 Sparse Group Lasso -- 3.5 Overlap Lasso -- 3.6 Group Lasso with Multiple Responses -- 3.7 Group Lasso Via Logistic Regression -- 3.8 Group Lasso for the Generalized Additive Models -- 4 Fused Lasso -- 4.1 Applications of Fused Lasso -- 4.2 Solving Fused Lasso Via Dynamic Programming -- 4.3 LARS -- 4.4 Dual Lasso Problem and Generalized Lasso -- 4.5 ADMM -- 5 Graphical Models -- 5.1 Graphical Models -- 5.2 Graphical Lasso -- 5.3 Estimation of the Graphical Model Based on the Quasi-Likelihood -- 5.4 Joint Graphical Lasso -- 6 Matrix Decomposition -- 6.1 Singular Decomposition -- 6.2 Eckart-Young's Theorem -- 6.3 Norm -- 6.4 Sparse Estimation for Low-Rank Estimations -- 7 Multivariate Analysis -- 7.1 Principal Component Analysis (1): SCoTLASS -- 7.2 Principle Component Analysis (2): SPCA -- 7.3 K-Means Clustering -- 7.4 Convex Clustering -- Appendix References. 606 $aMultivariate analysis 606 $aEstimation theory 606 $aPython (Computer program language) 615 0$aMultivariate analysis. 615 0$aEstimation theory. 615 0$aPython (Computer program language) 676 $a519.535 700 $aSuzuki$b Joe$0846228 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464436203316 996 $aSparse Estimation with Math and Python$92569690 997 $aUNISA LEADER 01680nam2 22003131i 450 001 RML0269323 005 20231121125724.0 100 $a20121121d1953 ||||0itac50 ba 101 | $aita 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $a˜Vol. 16.1: œStoria dei Papi nel periodo dell'Assolutismo dall'elezione di Benedetto 14. sino alla morte di Pio 6. (1740-1799)$dversione italiana di Pio Cenci$eBenedetto 14. e Clemente 13. (1740-1769)$fLudovico barone von Pastor 210 $aRoma $cDesclée & C. Ed. Pontifici $d1953 215 $aXX, 1053 p.$d26 cm 461 1$1001RML0269317$12001 $aStoria dei Papi dalla fine del Medio Evo$ecompilata col sussidio dell'Archivio segreto pontificio e di molti altri Archivi$fLudovico barone von Pastor 606 $aClemente $x1758-1769$2FIR$3RMLC406524$9I 606 $aBenedetto $x1740-1758$2FIR$3RMLC406525$9I 700 1$aPastor$b, Ludwig : von$c $3RMLV173667$0214612 702 1$aCenci$b, Pio$3RMLV173669 801 3$aIT$bIT-01$c20121121 850 $aIT-RM0251 $aIT-FR0017 899 $aBiblioteca Della Soprintendenza Archivistica Per Il Lazio$bRM0251 899 $aBiblioteca umanistica Giorgio Aprea$bFR0017 912 $aRML0269323 950 2$aBiblioteca umanistica Giorgio Aprea$d 52CIS 3/233.16.1$e 52VM 0000397105 VM barcode:00010296. - Inventario:1504 FLSVM$fB $h20040216$i20121204 977 $a 24$a 52 996 $aStoria dei papi nel periodo dell'assolutismo, dall'elezione di Benedetto 14. sino alla morte di Pio 6. (1740-1799)$91729854 997 $aUNICAS