LEADER 01194cam0-22004451i-450- 001 990006212430403321 005 20150123101428.0 035 $a000621243 035 $aFED01000621243 035 $a(Aleph)000621243FED01 035 $a000621243 100 $a20000112d1978----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------001yy 200 1 $aSopravvivere senza governare$ei partiti nel parlamento italiano$fGiuseppe Di Palma 210 $aBologna$cil Mulino$d1978 215 $a394 p.$d22 cm 225 1 $aStudi e ricerche$v86 676 $a324.245 676 $a19340 676 $a19350 676 $a19510 700 1$aDi Palma,$bGiuseppe$0118911 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006212430403321 952 $aDP XV-115$b6243$fDEC 952 $aCOLLEZ. 345 (86)$b23911*$fFGBC 952 $a19350 DIP$b10185/I$fSES 952 $aXXVI 985$b1092$fDDCIC 952 $aCOLLEZ. 103 (86)$b10385$fFSPBC 952 $aXII A 442$b16332$fFSPBC 959 $aDEC 959 $aFGBC 959 $aDDCIC 959 $aSES 959 $aFSPBC 996 $aSopravvivere senza governare$9647774 997 $aUNINA LEADER 01201nam2-2200385---450- 001 990002832970203316 005 20091117121544.0 010 $a1-85898-838-1 035 $a000283297 035 $aUSA01000283297 035 $a(ALEPH)000283297USA01 035 $a000283297 100 $a20061107h2000----km-y0itay50------ba 101 $aeng 102 $aGB 105 $a||||||||001yy 200 1 $a<<1.>> : States, markets and civil society in Asia Pacific$fJoseph A. Camilleri 210 $aCheltenham$cEdward Elgar$dcopyr. 2000 215 $aXVI, 475 p.$d24 cm 410 0$12001 461 1$1001000283296$12001 607 $aAsia orientale$xEconomia 676 $a382.41 700 1$aCAMILLERI,$bJoseph A.$0119957 801 0$aIT$bsalbc$gISBD 912 $a990002832970203316 951 $a382.41 CAM 1 1 (IG VIII 10 ING 927/1)$b49535 G.$cIG VIII 10$d00118798 959 $aBK 969 $aECO 979 $aIANNONE$b90$c20061107$lUSA01$h1054 979 $aIANNONE$b90$c20061107$lUSA01$h1056 979 $aIANNONE$b90$c20061107$lUSA01$h1100 979 $aRSIAV4$b90$c20091117$lUSA01$h1215 996 $aStates, markets and civil society in Asia Pacific$9993715 997 $aUNISA LEADER 05926nam 22005175 450 001 9910157631903321 005 20250504232525.0 010 $a981-10-2164-3 024 7 $a10.1007/978-981-10-2164-0 035 $a(CKB)3710000001001554 035 $a(DE-He213)978-981-10-2164-0 035 $a(MiAaPQ)EBC4774258 035 $a(PPN)197454038 035 $a(EXLCZ)993710000001001554 100 $a20161229d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNew Theory of Discriminant Analysis After R. Fisher $eAdvanced Research by the Feature Selection Method for Microarray Data /$fby Shuichi Shinmura 205 $a1st ed. 2016. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2016. 215 $a1 online resource (XX, 208 p. 28 illus., 25 illus. in color.) 311 08$a981-10-2163-5 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $a1 New Theory of Discriminant Analysis -- 1.1 Introduction -- 1.2 Motivation for our Research -- 1.3 Discriminant Functions -- 1.4 Unresolved Problem (Problem 1) -- 1.5 LSD Discrimination (Problem 2) -- 1.6 Generalized Inverse Matrices (Problem 3) -- 1.7 K-fold Cross-validation (Problem 4) -- 1.8 Matroska Feature Selection Method (Problem 5) -- 1.9 Summary -- References -- 2 Iris Data and Fisher?s Assumption -- 2.1 Introduction -- 2.2 Iris Data -- 2.3 Comparison of Seven LDFs -- 2.4 100-folf Cross-validation for Small Sample Method (Method 1) -- 2.5 Summary -- References -- 3 The Cephalo-Pelvic Disproportion (CPD) Data with Collinearity -- 3.1 Introduction -- 3.2 CPD Data -- 3.3 100-folf Cross-validation -- 3.4 Trial to Remove Collinearity -- 3.5 Summary -- References -- 4 Student Data and Problem 1 -- 4.1 Introduction -- 4.2 Student Data -- 4.3 100-folf Cross-validation for Student Data -- 4.4 Student Linearly Separable Data -- 4.5 Summary -- References -- 5 The Pass/Fail Determination using Exam Scores -A Trivial Linear Discriminant Function -- 5.1 Introduction -- 5.2 Pass/Fail Determination by Exam Scores Data in 2012 -- 5.3 Pass/Fail Determination by Exam Scores (50% Level in 2012) -- 5.4 Pass/Fail Determination by Exam Scores (90% Level in 2012) -- 5.5 Pass/Fail Determination by Exam Scores (10% Level in 2012) -- 5.6 Summary -- 6 Best Model for the Swiss Banknote Data ? Explanation 1 of Matroska Feature -selection Method (Method 2) -. References -- 6 Best Model for Swiss Banknote Data -- 6.1 Introduction -- 6.2 Swiss Banknote Data -- 6.3 100-folf Cross-validation for Small Sample Method -- 6.4 Explanation 1 for Swiss Banknote Data -- 6.5 Summary -- References -- 7 Japanese Automobile Data ? Explanation 2 of Matroska Feature Selection Method (Method 2) -- 7.1 Introduction -- 7.2 Japanese Automobile Data -- 7.3 100-folf Cross-validation (Method 1) -- 7.4 Matroska Feature Selection Method (Method 2) -- 7.5 Summary -- References -- 8 Matroska Feature Selection Method for Microarray Data (Method 2) -- 8.1 Introduction.-8.2 Matroska Feature Selection Method (Method2) -- 8.3 Results of the Golub et al. Dataset -- 8.4 How to Analyze the First BGS -- 8.5 Statistical Analysis of SM1 -- 8.6 Summary -- References -- 9 LINGO Program 1 of Method 1 -- 9.1 Introduction -- 9.2 Natural (Mathematical) Notation by LINGO -- 9.3 Iris Data in Excel -- 9.4 Six LDFs by LINGO -- 9.5 Discrimination of Iris Data by LINGO -- 9.6 How to Generate Re-sampling Samples and Prepare Data in Excel File -- 9.7 Set Model by LINGO -- Index. 330 $aThis is the first book to compare eight LDFs by different types of datasets, such as Fisher?s iris data, medical data with collinearities, Swiss banknote data that is a linearly separable data (LSD), student pass/fail determination using student attributes, 18 pass/fail determinations using exam scores, Japanese automobile data, and six microarray datasets (the datasets) that are LSD. We developed the 100-fold cross-validation for the small sample method (Method 1) instead of the LOO method. We proposed a simple model selection procedure to choose the best model having minimum M2 and Revised IP-OLDF based on MNM criterion was found to be better than other M2s in the above datasets. We compared two statistical LDFs and six MP-based LDFs. Those were Fisher?s LDF, logistic regression, three SVMs, Revised IP-OLDF, and another two OLDFs. Only a hard-margin SVM (H-SVM) and Revised IP-OLDF could discriminate LSD theoretically (Problem 2). We solved the defect of the generalized inverse matrices (Problem 3). For more than 10 years, many researchers have struggled to analyze the microarray dataset that is LSD (Problem 5). If we call the linearly separable model "Matroska," the dataset consists of numerous smaller Matroskas in it. We develop the Matroska feature selection method (Method 2). It finds the surprising structure of the dataset that is the disjoint union of several small Matroskas. Our theory and methods reveal new facts of gene analysis. 606 $aStatistics 606 $aBiometry 606 $aSocial sciences$xStatistical methods 606 $aStatistical Theory and Methods 606 $aBiostatistics 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 615 0$aStatistics. 615 0$aBiometry. 615 0$aSocial sciences$xStatistical methods. 615 14$aStatistical Theory and Methods. 615 24$aBiostatistics. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 676 $a519.5 700 $aShinmura$b Shuichi$4aut$4http://id.loc.gov/vocabulary/relators/aut$0756006 906 $aBOOK 912 $a9910157631903321 996 $aNew theory of discriminant analysis after R. Fisher$91523499 997 $aUNINA