Online Learning and Adaptive Filters
Online Learning and Adaptive Filters
Marcello L. R. de Campos, Universidade Federal do Rio de Janeiro
Wallace A. Martins, University of Luxembourg
Markus V. S. Lima, Universidade Federal do Rio de Janeiro
Jose A. Apolinário, Jr, Military Institute of Engineering
$105.00
USDLearn 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
Available
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.
