
Online Learning and Adaptive Filters
Cambridge University Press
Published on 8. December 2022
Book
Hardback
268 pages
978-1-108-84212-9 (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
Product notice
sewn/stitched
Cloth over boards
Illustrations
Worked examples or Exercises
Dimensions
Height: 244 mm
Width: 170 mm
Thickness: 16 mm
Weight
626 gr
ISBN-13
978-1-108-84212-9 (9781108842129)
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
E-Book
11/2022
Cambridge University Press
€98.49
Available for download
Persons
Paulo S. R. Diniz is a Professor at the Universidade Federal do Rio de Janeiro and a Fellow of the IEEE and of EURASIP. He is a Senior Editor of the IEEE Open Journal of Signal Processing and is co-author of a CUP textbook on Digital Signal Processing. He is also a member of the National Academy of Engineering and the Brazilian Academy of Science.
Author
Universidade Federal do Rio de Janeiro
Universidade Federal do Rio de Janeiro
University of Luxembourg
Universidade Federal do Rio de Janeiro
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.