Coefficient of Variation and Machine Learning Applications
Seiten
2019
CRC Press (Verlag)
978-0-367-27328-6 (ISBN)
CRC Press (Verlag)
978-0-367-27328-6 (ISBN)
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.
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.
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.
K. Hima Bindu, Raghava Morusupalli, Nilanjan Dey, C. Raghavendra Rao
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.
Erscheinungsdatum | 05.12.2019 |
---|---|
Reihe/Serie | Intelligent Signal Processing and Data Analysis |
Zusatzinfo | 24 Tables, black and white; 30 Illustrations, black and white |
Verlagsort | London |
Sprache | englisch |
Maße | 138 x 216 mm |
Gewicht | 294 g |
Themenwelt | Mathematik / Informatik ► Informatik ► Theorie / Studium |
Mathematik / Informatik ► Mathematik | |
Technik ► Elektrotechnik / Energietechnik | |
Technik ► Umwelttechnik / Biotechnologie | |
ISBN-10 | 0-367-27328-4 / 0367273284 |
ISBN-13 | 978-0-367-27328-6 / 9780367273286 |
Zustand | Neuware |
Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
Grundlagen – Anwendungen – Perspektiven
Buch | Softcover (2022)
Springer Vieweg (Verlag)
34,99 €
Eine Einführung in die Systemtheorie
Buch | Softcover (2022)
UTB (Verlag)
25,00 €