Linked e-resources
Details
Table of Contents
Intro; Preface; Acknowledgements; Contents; Abbreviations; Symbols; Our Research Theme: Discrimination of two classes (n Cases and p Variables) by Eight LDFs and QDF; Two Facts of Theory; Six Mathematical Programming (MP)-Based LDFs by LINGO; Two Methods; Important Statistics; LINGO Programs:; Discriminant Functions by JMP; Seven Problems and Four Facts; 1 New Theory of Discriminant Analysis and Cancer Gene Analysis; 1.1 Introduction; 1.2 Fundamental of Theory; 1.2.1 The Motivation of Our Research; 1.2.2 IP-OLDF Based on MNM Criterion and Two Facts; 1.2.3 Simple Example
1.2.4 Ordinary LP Solution1.3 Five Serious Problems and Three Excuses; 1.3.1 Four Problems; 1.3.2 Problem5; 1.3.3 Three Excuses of Cancer Gene Analysis; 1.4 Four OLDFs and MNM Instead of NM; 1.4.1 Revised IP-OLDF and the Defects of Number of Misclassifications; 1.4.2 Revised LP-OLDF and Revised IPLP-OLDF; 1.4.3 Hard-Margin SVM (H-SVM); 1.4.4 Soft-Margin SVM (S-SVM); 1.4.5 Statisticians Claim for MP-Based LDFs; 1.5 Matryoshka Feature Selection Method (Method2) and RatioSV; 1.5.1 Method2; 1.5.2 RatioSV: Measurement of the Degree of Linear Separability; 1.5.3 Six Famous Microarrays
1.5.4 How to Develop Method2 (a Surprising 54-Day Research Diary)1.5.5 Results of Six Microarrays; 1.5.6 The Reason for Natural Feature Selection; 1.5.7 Two New Facts; 1.6 Validation of Method2 by Common Data; 1.6.1 Matryoshka Structure of Swiss Banknote Data; 1.6.2 Validation of LINGO Program3 Results; 1.6.3 Validation of Method2 by Japanese 44 Cars Data; 1.6.4 Examination of Duplicate Data; 1.7 Conclusion; References; 2 Overview of Cancer Gene Diagnosis; 2.1 Introduction; 2.2 Cancer Gene Diagnosis; 2.3 Analysis of 64 SMs Obtained by Alon's Microarray; 2.3.1 Analysis of 64 SMs
2.3.2 Analysis of RipDS8 by Standard Statistical Methods2.4 Analysis of 64 RipDSs Data; 2.4.1 Examination of 64 RipDSs and RatioSV of RIP; 2.4.2 Ward Cluster Analysis of RipDSs New Data; 2.4.3 PCA Results of New Data; 2.5 The 130 BGSs of Alon's Microarray; 2.5.1 Results by Standard Statistical Methods; 2.5.2 Examination of RipDSs of 130 BGSs; 2.5.3 Examination of RipDSs New Data by PCA and Cluster Analysis; 2.5.4 Summary; 2.6 Other Five Microarrays; 2.6.1 Singh's Microarray; 2.6.2 Golub Microarray; 2.6.3 Tian's Microarray; 2.6.4 Chiaretti Microarray; 2.6.5 Shipp Microarray; 2.7 Conclusion
1.2.4 Ordinary LP Solution1.3 Five Serious Problems and Three Excuses; 1.3.1 Four Problems; 1.3.2 Problem5; 1.3.3 Three Excuses of Cancer Gene Analysis; 1.4 Four OLDFs and MNM Instead of NM; 1.4.1 Revised IP-OLDF and the Defects of Number of Misclassifications; 1.4.2 Revised LP-OLDF and Revised IPLP-OLDF; 1.4.3 Hard-Margin SVM (H-SVM); 1.4.4 Soft-Margin SVM (S-SVM); 1.4.5 Statisticians Claim for MP-Based LDFs; 1.5 Matryoshka Feature Selection Method (Method2) and RatioSV; 1.5.1 Method2; 1.5.2 RatioSV: Measurement of the Degree of Linear Separability; 1.5.3 Six Famous Microarrays
1.5.4 How to Develop Method2 (a Surprising 54-Day Research Diary)1.5.5 Results of Six Microarrays; 1.5.6 The Reason for Natural Feature Selection; 1.5.7 Two New Facts; 1.6 Validation of Method2 by Common Data; 1.6.1 Matryoshka Structure of Swiss Banknote Data; 1.6.2 Validation of LINGO Program3 Results; 1.6.3 Validation of Method2 by Japanese 44 Cars Data; 1.6.4 Examination of Duplicate Data; 1.7 Conclusion; References; 2 Overview of Cancer Gene Diagnosis; 2.1 Introduction; 2.2 Cancer Gene Diagnosis; 2.3 Analysis of 64 SMs Obtained by Alon's Microarray; 2.3.1 Analysis of 64 SMs
2.3.2 Analysis of RipDS8 by Standard Statistical Methods2.4 Analysis of 64 RipDSs Data; 2.4.1 Examination of 64 RipDSs and RatioSV of RIP; 2.4.2 Ward Cluster Analysis of RipDSs New Data; 2.4.3 PCA Results of New Data; 2.5 The 130 BGSs of Alon's Microarray; 2.5.1 Results by Standard Statistical Methods; 2.5.2 Examination of RipDSs of 130 BGSs; 2.5.3 Examination of RipDSs New Data by PCA and Cluster Analysis; 2.5.4 Summary; 2.6 Other Five Microarrays; 2.6.1 Singh's Microarray; 2.6.2 Golub Microarray; 2.6.3 Tian's Microarray; 2.6.4 Chiaretti Microarray; 2.6.5 Shipp Microarray; 2.7 Conclusion