Online Learning and Adaptive Filters
Learn to solve the unprecedented challenges facing Online Learning and Adaptive Signal Processing in this concise, intuitive text. The ever-increasing amount of data generated every day requires new strategies to tackle issues such as: combining data from a large number of sensors; improving spectral usage, utilizing multiple-antennas with adaptive capabilities; or learning from signals placed on graphs, generating unstructured data. Solutions to all of these and more are described in a condensed and unified way, enabling you to expose valuable information from data and signals in a fast and economical way. The up-to-date techniques explained here can be implemented in simple electronic hardware, or as part of multi-purpose systems. Also featuring alternative explanations for online learning, including newly developed methods and data selection, and several easily implemented algorithms, this one-of-a-kind book is an ideal resource for graduate students, researchers, and professionals in online learning and adaptive filtering.
- Offers practical solutions to challenging problems, including algorithms that can be readily applied to simple electronic hardware or as part of multi-purpose systems
- Describes alternative explanations for online learning, including newly developed methods and data selection
Product details
December 2022Hardback
9781108842129
300 pages
251 × 175 × 19 mm
0.63kg
Not yet published - available from February 2025
Table of Contents
- 1. Introduction
- 2. Adaptive filtering for sparse models
- 3. Kernel-based adaptive filtering
- 4. Distributed adaptive filters
- 5. Adaptive beamforming
- 6. Adaptive filtering on graphs.