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Online Learning and Adaptive Filters
Diniz, Paulo S. R.; de Campos, Marcello L. R.; Alves Martins, Wallace et al.
2022Cambridge University Press
 

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Abstract :
[en] 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.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Diniz, Paulo S. R.;  Federal University of Rio de Janeiro
de Campos, Marcello L. R.;  Federal University of Rio de Janeiro
Alves Martins, Wallace ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Lima, Markus V. S.;  Federal University of Rio de Janeiro
Apolinário Jr., José A.;  Instituto Militar de Engenharia
External co-authors :
yes
Language :
English
Title :
Online Learning and Adaptive Filters
Publication date :
2022
Publisher :
Cambridge University Press
ISBN/EAN :
978-1-108-84212-9
Number of pages :
250
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 07 December 2022

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