K. Hima Bindu, Raghava Morusupalli, Nilanjan Dey, C. Raghavendra Rao
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.
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1.Focuses on the Coefficient of Variation (CV) and its utility in the context of Machine Learning and Big Data Analysis
2.Provides understanding of efficient deillegalscriptive statistics of real world data drawn from diversified applications
3.Includes a separate chapter covering various dispersion measures and their classification
4.Solves feature selection problem by implementing a decision tree
5.Elaborates novel real world applications spanning from variational to regularization methods
1. Introduction to Statistical Dispersion 2. Coefficient of Variation 3. Coefficient of Variation Computational Strategies 4. Coefficient of Variation Based Image Representation 5. Coefficient of Variation based Decision Tree (CvDT) 6. Some Applications.
This book explains computational strategies, properties of Coefficient of Variation (CV) and related metadata extraction. It includes representational/classification strategies through illustrative explanations. CV in context of contemporary Machine Learning strategies and Big Data paradigms is explained through selected applications.
Inhaltsverzeichnis
1. Introduction to Statistical Dispersion 2. Coefficient of Variation 3. Coefficient of Variation Computational Strategies 4. Coefficient of Variation Based Image Representation 5. Coefficient of Variation based Decision Tree (CvDT) 6. Some Applications.
Klappentext
This book explains computational strategies, properties of Coefficient of Variation (CV) and related metadata extraction. It includes representational/classification strategies through illustrative explanations. CV in context of contemporary Machine Learning strategies and Big Data paradigms is explained through selected applications.
