Coefficient of Variation and Machine Learning Applications

Chapter 1 Introduction to Coefficient of Variation 
1.1 INTRODUCTION
1.2 COEFFICIENT OF VARIATION 
1.3 NORMALIZATION 3 
1.4 PROPERTIES OF COEFFICIENT OF VARIATION
1.5 LIMITATIONS OF COEFFICIENT OF VARIATION 
1.6 CV INTERPRETATION
1.7 SUMMARY
1.8 EXERCISES 
Chapter 2 CV Computational Strategies 
2.1 INTRODUCTION
2.2 CV COMPUTATION OF POOLED DATA 
2.3 COMPARISON OF CV WITH ENTROPYAND GINI INDEX
2.4 CV FOR CATEGORICAL VARIABLES
2.5 CVCOMPUTATIONBYMAP-REDUCESTRATEGIES 
2.6 SUMMARY 
2.7 EXERCISES
Chapter 3 Image Representation
3.1 INTRODUCTION 
3.2 CVIMAGE 
3.3 CV FEATURE VECTOR 
3.4 SUMMARY 
3.5 EXERCISES 
Chapter 4 Supervised Learning
4.1 INTRODUCTION
4.2 PRE-PROCESSING (DECISION ATTRIBUTE CALIBRATION)
4.3 CONDITIONAL CV
4.4 CVGAIN (CV FOR ATTRIBUTE SELECTION) 
4.5 ATTRIBUTE ORDERING WITH CVGAIN 
4.6 CVDT FOR CLASSIFICATION 
4.7 CVDT FOR REGRESSION 
4.8 CVDT FOR BIG DATA 
4.9 FUZZY CVDT 
4.10 SUMMARY 
4.11 EXERCISES
Chapter 5 Applications
5.1 IMAGE CLUSTERING
5.2 IMAGE SEGMENTATION
5.3 FEATURE SELECTION
5.4 MOOD ANALYSIS 
5.5 CV FOR OPTIMIZATION
5.6 HEALTH CARE 
5.7 SOCIAL NETWORK 
5.8 SUMMARY 
5.9 EXERCISES