New theory of discriminant analysis after R. Fisher : advanced research by the feature selection method for microarray data /: advanced research by the feature selection method for microarray data. ([2016])
- Record Type:
- Book
- Title:
- New theory of discriminant analysis after R. Fisher : advanced research by the feature selection method for microarray data /: advanced research by the feature selection method for microarray data. ([2016])
- Main Title:
- New theory of discriminant analysis after R. Fisher : advanced research by the feature selection method for microarray data
- Further Information:
- Note: Shuichi Shinmura.
- Other Names:
- Shinmura, Shuichi
- Contents:
- Preface; Acknowledgments; Contents; Symbols; 1 New Theory of Discriminant Analysis; 1.1 Introduction; 1.1.1 Theory Theme; 1.1.2 Five Problems; 1.1.2.1 Problem 1; 1.1.2.2 Problem 2; 1.1.2.3 Problem 3; 1.1.2.4 Problem 4; 1.1.2.5 Problem 5; 1.1.2.6 Summary; 1.2 Motivation for Our Research; 1.2.1 Contribution by Fisher; 1.2.2 Defect of Fisher's Assumption for Medical Diagnosis; 1.2.3 Research Outlook; 1.2.4 Method 1 and Problem 4; 1.3 Discriminant Functions; 1.3.1 Statistical Discriminant Functions; 1.3.2 Before and After SVM; 1.3.3 IP-OLDF and Four New Facts of Discriminant Analysis. 1.3.4 Revised IP-OLDF, Revised LP-OLDF, and Revised IPLP-OLDF1.4 Unresolved Problem (Problem 1); 1.4.1 Perception Gap of Problem 1; 1.4.2 Student Data; 1.5 LSD Discrimination (Problem 2); 1.5.1 Importance of This Problem; 1.5.2 Pass/Fail Determination; 1.5.3 Discrimination by Four Testlets; 1.6 Generalized Inverse Matrices (Problem 3); 1.7 K-Fold Cross-Validation (Problem 4); 1.7.1 100-Fold Cross-Validation; 1.7.2 LOO and K-Fold Cross-Validation; 1.8 Matroska Feature-Selection Method (Problem 5); 1.9 Summary; References; 2 Iris Data and Fisher's Assumption; 2.1 Introduction. 2.1.1 Evaluation of Iris Data2.1.2 100-Fold Cross-Validation for Small Sample (Method 1); 2.2 Iris Data; 2.2.1 Data Outlook; 2.2.2 Model Selection by Regression Analysis; 2.3 Comparison of Seven LDFs; 2.3.1 Comparison of MNM and Eight NMs; 2.3.2 Comparison of Seven Discriminant Coefficient; 2.3.3 LINGO Program 1: Six MP-BasedPreface; Acknowledgments; Contents; Symbols; 1 New Theory of Discriminant Analysis; 1.1 Introduction; 1.1.1 Theory Theme; 1.1.2 Five Problems; 1.1.2.1 Problem 1; 1.1.2.2 Problem 2; 1.1.2.3 Problem 3; 1.1.2.4 Problem 4; 1.1.2.5 Problem 5; 1.1.2.6 Summary; 1.2 Motivation for Our Research; 1.2.1 Contribution by Fisher; 1.2.2 Defect of Fisher's Assumption for Medical Diagnosis; 1.2.3 Research Outlook; 1.2.4 Method 1 and Problem 4; 1.3 Discriminant Functions; 1.3.1 Statistical Discriminant Functions; 1.3.2 Before and After SVM; 1.3.3 IP-OLDF and Four New Facts of Discriminant Analysis. 1.3.4 Revised IP-OLDF, Revised LP-OLDF, and Revised IPLP-OLDF1.4 Unresolved Problem (Problem 1); 1.4.1 Perception Gap of Problem 1; 1.4.2 Student Data; 1.5 LSD Discrimination (Problem 2); 1.5.1 Importance of This Problem; 1.5.2 Pass/Fail Determination; 1.5.3 Discrimination by Four Testlets; 1.6 Generalized Inverse Matrices (Problem 3); 1.7 K-Fold Cross-Validation (Problem 4); 1.7.1 100-Fold Cross-Validation; 1.7.2 LOO and K-Fold Cross-Validation; 1.8 Matroska Feature-Selection Method (Problem 5); 1.9 Summary; References; 2 Iris Data and Fisher's Assumption; 2.1 Introduction. 2.1.1 Evaluation of Iris Data2.1.2 100-Fold Cross-Validation for Small Sample (Method 1); 2.2 Iris Data; 2.2.1 Data Outlook; 2.2.2 Model Selection by Regression Analysis; 2.3 Comparison of Seven LDFs; 2.3.1 Comparison of MNM and Eight NMs; 2.3.2 Comparison of Seven Discriminant Coefficient; 2.3.3 LINGO Program 1: Six MP-Based LDFs for Original Data; 2.4 100-Fold Cross-Validation for Small Sample Method (Method 1); 2.4.1 Four Trials to Obtain Validation Sample; 2.4.1.1 Generate Training and Validation Samples by Random Number; 2.4.1.2 20, 000 Normal Random Sampling. 2.4.1.3 20, 000 Resampling Samples2.4.1.4 K-Fold Cross-Validation for Small Sample Method; 2.4.2 Best Model Comparison; 2.4.3 Comparison of Discriminant Coefficient; 2.5 Summary; References; 3 Cephalo-Pelvic Disproportion Data with Collinearities; 3.1 Introduction; 3.2 CPD Data; 3.2.1 Collinearities; 3.2.2 How to Find Linear Relationships in Collinearities; 3.2.3 Comparison Between MNM and Eight NMs; 3.2.4 Comparison of 95€% CI of Discriminant Coefficient; 3.3 100-Fold Cross-Validation; 3.3.1 Best Model; 3.3.2 95€% CI of Discriminant Coefficient; 3.4 Trial to Remove Collinearity. 3.4.1 Examination by PCA (Alternative 2)3.4.2 Third Alternative Approach; 3.5 Summary; References; 4 Student Data and Problem 1; 4.1 Introduction; 4.2 Student Data; 4.2.1 Data Outlook; 4.2.2 Different LDFs; 4.2.3 Comparison of Seven LDFs; 4.2.4 K-Best Option; 4.2.5 Evaluation by Regression Analysis; 4.3 100-Fold Cross-Validation of Student Data; 4.3.1 Best Model; 4.3.2 Comparison of Coefficients by LINGO Program 1 and Program 2; 4.4 Student Linearly Separable Data; 4.4.1 Comparison of MNM and Nine "Diff1s"; 4.4.2 Best Model; 4.4.3 95 % CI of Discriminant Coefficient; 4.5 Summary; References. … (more)
- Publisher Details:
- Singapore : Springer
- Publication Date:
- 2016
- Extent:
- 1 online resource (xx, 208 pages), illustrations
- Subjects:
- 519.535
Discriminant analysis
MATHEMATICS -- Applied
MATHEMATICS -- Probability & Statistics -- General
Discriminant analysis
Statistics
Statistical Theory and Methods
Statistics for Life Sciences, Medicine, Health Sciences
Biostatistics
Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law
Electronic books - Languages:
- English
- ISBNs:
- 9789811021640
9811021643
9811021635
9789811021633 - Related ISBNs:
- 9789811021633
- Notes:
- Note: Includes bibliographical references and index.
Note: Online resource; title from PDF title page (SpringerLink, viewed January 9, 2017). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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- Available online (eLD content is only available in our Reading Rooms) ↗
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- British Library HMNTS - ELD.DS.405436
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- 02_476.xml