Online Learning and Adaptive Filters

This text discusses new strategies for tackling challenges in online learning and adaptive signal processing,such as combining data from multiple sensors, improving spectral usage, and learning from signals placed on graphs. It provides up-to-date techniques that can be implemented in simple electronic hardware or multi-purpose systems, and features alternative explanations for online learning, including newly developed methods and data selection. It is an ideal resource for graduate students, researchers, and professionals in these fields.

Format: Hardback
Length: 300 pages
Publication date: 08 December 2022
Publisher: Cambridge University Press

The ever-growing volume of data generated daily necessitates innovative approaches to address challenges such as:

- Combining data from numerous sensors
- Enhancing spectral utilization through the use of multiple antennas with adaptive capabilities
- Learning from signals represented on graphs, resulting in the generation of unstructured data

Solutions to these and other complex problems are presented in a concise and cohesive manner, enabling efficient extraction of valuable information from data and signals. The techniques explained in this book are up-to-date and can be implemented in straightforward electronic hardware or as part of versatile multi-purpose systems. Additionally, alternative explanations for online learning, including newly developed methods and data selection, are provided, along with several easily implemented algorithms. This unique book serves as an invaluable resource for graduate students, researchers, and professionals engaged in online learning and adaptive filtering.

Weight: 653g
ISBN-13: 9781108842129