Online Learning and Adaptive Filters
Publisher: Cambridge University Press
Date of Publication: 8 December 2022
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Product details:
ISBN13: | 9781108842129 |
ISBN10: | 1108842127 |
Binding: | Hardback |
No. of pages: | 300 pages |
Size: | 251x175x19 mm |
Weight: | 630 g |
Language: | English |
577 |
Category:
Short description:
Discover up-to-date techniques and algorithms in this concise, intuitive text, with extensive solutions for challenging learning problems.
Long description:
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.
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.
-based adaptive filtering; 4. Distributed adaptive filters; 5. Adaptive beamforming; 6. Adaptive filtering on graphs.