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.
Coefficient of Variation and Machine Learning Applications
148
Coefficient of Variation and Machine Learning Applications
148Related collections and offers
Product Details
| ISBN-13: | 9781000752625 |
|---|---|
| Publisher: | CRC Press |
| Publication date: | 11/20/2019 |
| Series: | Intelligent Signal Processing and Data Analysis |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 148 |
| File size: | 17 MB |
| Note: | This product may take a few minutes to download. |