Online Learning and Adaptive Filters

Discover up-to-date techniques and algorithms in this concise, intuitive text, with extensive solutions for challenging learning problems.

Paulo S. R. Diniz (Author), Marcello L. R. de Campos (Author), Wallace A. Martins (Author), Markus V. S. Lima (Author), Jose A. Apolinário, Jr (Author)

9781108842129, Cambridge University Press

Hardback, published 8 December 2022

300 pages
25.1 x 17.5 x 1.9 cm, 0.63 kg

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.

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.

Subject Areas: Signal processing [UYS], Systems analysis & design [UYD], Distributed systems [UTR], Cybernetics & systems theory [GPFC]