1st Edition
Coefficient of Variation and Machine Learning Applications
Chapter 1 Introduction to Coef¿cient 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
Biography
K. Hima Bindu, Raghava Morusupalli, Nilanjan Dey, C. Raghavendra Rao
