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E-raamat: Coefficient of Variation and Machine Learning Applications [Taylor & Francis e-raamat]

(Deaprtmet of Computer Science and Engineering, NIT Andhra Pradesh, India), , ,
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Coefficient of Variation and Machine Learning ApplicationsSuurem pilt
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Coefficient of Variation (CV) is a unit free index indicating the consistency of the data associated with a real-world process and is simple to mold into computational paradigms. This book provides necessary exposure of computational strategies, properties of CV and extracting the metadata leading to efficient knowledge representation. It also compiles representational and classification strategies based on the CV through illustrative explanations. The potential nature of CV in the context of contemporary Machine Learning strategies and the Big Data paradigms is demonstrated through selected applications. Overall, this book explains statistical parameters and knowledge representation models.

List of Figures
ix
List of Tables
xi
Preface xiii
Authors xvii
Chapter 1 Introduction to Coefficient of Variation
1(26)
1.1 Introduction
1(2)
1.2 Coefficient Of Variation
3(2)
1.3 Normalization
5(4)
1.3.1 Coefficient of Variation of Normalized Variable
5(2)
1.3.2 Illustration
7(1)
1.3.3 Random Variable with Probability Density Function
8(1)
1.3.3.1 Normal Distribution with Mean μ(≠ 0) and Standard Deviation σ
8(1)
1.3.3.2 Gamma Distribution with Mean μ and Standard Deviation σ
8(1)
1.3.4 Random Variable with Probability Mass Function
8(1)
1.4 Properties Of Coefficient Of Variation
9(13)
1.4.1 Properties of Mean
9(1)
1.4.2 Properties of Standard Deviation
9(1)
1.4.3 Properties of CV
9(1)
1.4.4 Features Based on Coefficient of Variation
10(1)
1.4.4.1 Influence of Translation and Scale on Features of CV
11(2)
1.4.5 CV of Mixture Distributions
13(3)
1.4.5.1 Mixture of Normal Distributions
16(6)
1.5 Limitations Of Coefficient Of Variation
22(1)
1.6 CV Interpretation
22(1)
1.7 Summary
23(1)
1.8 Exercises
24(3)
Chapter 2 CV Computational Strategies
27(16)
2.1 Introduction
27(1)
2.2 CV Computation Of Pooled Data
28(3)
2.2.1 Illustration
30(1)
2.3 Comparison Of Cv With Entropy And Gini Index
31(1)
2.4 CV For Categorical Variables
32(4)
2.4.1 Table Lookup Method
33(1)
2.4.2 Mapping Method
34(1)
2.4.3 Zero Avoiding Calibration
35(1)
2.5 CV Computation By Mapreduce Strategies
36(2)
2.6 Summary
38(1)
2.7 Exercises
38(5)
Chapter 3 Image Representation
43(14)
3.1 Introduction
43(1)
3.2 CVIMAGE
44(4)
3.2.1 CV Image Representation of Lena Image
45(3)
3.3 CV Feature Vector
48(7)
3.3.1 Demonstration
50(1)
3.3.2 Hvalue Distribution Analysis
51(3)
3.3.3 Ranking of CV Features
54(1)
3.4 Summary
55(1)
3.5 Exercises
55(2)
Chapter 4 Supervised Learning
57(22)
4.1 Introduction
57(1)
4.2 Preprocessing (Decision Attribute Calibration)
58(1)
4.3 Conditional Cv
59(2)
4.4 Cvgain (Cv For Attribute Selection)
61(1)
4.4.1 Example
61(1)
4.5 Attribute Ordering With Cvgain
61(2)
4.6 Cvdt For Classification
63(5)
4.6.1 Cvdt Algorithm
64(1)
4.6.2 Cvdt Example
64(3)
4.6.3 Using Cvdt For Classification
67(1)
4.7 Cvdt For Regression
68(3)
4.8 Cvdt For Big Data
71(2)
4.8.1 Distributed Cvdt Induction With Horizontal Fragmentation
71(1)
4.8.2 Distributed Cvdt Induction With Vertical Fragmentation
72(1)
4.9 Fuzzy Cvdt
73(3)
4.10 Summary
76(1)
4.11 Exercises
76(3)
Chapter 5 Applications
79(10)
5.1 Image Clustering
79(3)
5.2 Image Segmentation
82(1)
5.3 Feature Selection
82(2)
5.4 Mood Analysis
84(1)
5.4.1 Bipolar Disorder
84(1)
5.4.2 Twitter Mood Predicts The Stock Market
84(1)
5.5 Cv For Optimization
85(1)
5.6 Health Care
86(1)
5.7 Social Network
87(1)
5.8 Summary
87(1)
5.9 Exercises
87(2)
Appendix A 89(32)
References 121(4)
Index 125
K. Hima Bindu, Raghava Morusupalli, Nilanjan Dey, C. Raghavendra Rao
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