Online Learning and Adaptive Filters
Published on 23. November 2022
978-1-108-90224-3 (ISBN)
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.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Illustrations
Worked examples or Exercises
File size
6,42 MB
ISBN-13
978-1-108-90224-3 (9781108902243)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Paulo S. R. Diniz | Marcello L. R. de Campos | Wallace A. Martins
Online Learning and Adaptive Filters
Book
12/2022
Cambridge University Press
€108.50
Shipment within 15-20 days
Persons
Content
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.
