Coefficient of Variation and Machine Learning Applications

1. Auflage 2019
ISBN: 978-0-367-27328-6
Verlag: Taylor & Francis Ltd

Buch, Englisch, 148 Seiten, Format (B × H): 223 mm x 144 mm, Gewicht: 306 g

Reihe: Intelligent Signal Processing and Data Analysis

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.

Autoren/Hrsg.

Dey, Nilanjan

Morusupalli, Raghava

Rao, C. Raghavendra

Hima Bindu, K.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik Mathematik Stochastik

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.

Über Autor(innen)

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