Coefficient of Variation and Machine Learning Applications

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 contents

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.

About the author

K. Hima Bindu, Raghava Morusupalli, Nilanjan Dey, C. Raghavendra Rao

Summary

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.

Product details

• Authors: Nilanjan Dey, Dey Nilanjan, K. Hima Bindu, K. Morusupalli Hima Bindu, Raghava Morusupalli, C. Raghavendra Rao
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Paperback / Softback
• Released: 30.06.2021
• EAN: 9781032084190
• ISBN: 978-1-0-3208419-0
• No. of pages: 148
• Series: Intelligent Signal Processing and Data Analysis
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > General, dictionaries; MATHEMATICS / Probability & Statistics / General, TECHNOLOGY & ENGINEERING / Electrical, MATHEMATICS / Complex Analysis, COMPUTERS / Machine Theory, COMPUTERS / Computer Engineering, Probability & statistics, Information technology: general issues, Automatic control engineering, Probability and statistics, Information technology: general topics