Online Learning and Adaptive Filters

Diniz, Paulo S. R.; de Campos, Marcello L. R.; ALVES MARTINS, Wallace; Lima, Markus V. S.; Apolinário Jr., José A.

doi:10.1017/9781108896139

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Online Learning and Adaptive Filters

Diniz, Paulo S. R.; de Campos, Marcello L. R.; ALVES MARTINS, Wallace et al.

2022 • Cambridge University Press

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https://hdl.handle.net/10993/52999

DOI
10.1017/9781108896139

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Résumé :

[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 :

Ingénierie électrique & électronique

Auteur, co-auteur :

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

Co-auteurs externes :

yes

Langue du document :

Anglais

Titre :

Online Learning and Adaptive Filters

Date de publication/diffusion :

2022

Maison d'édition :

Cambridge University Press

ISBN/EAN :

978-1-108-84212-9

Nombre de pages :

250

Focus Area :

Security, Reliability and Trust

URL complémentaire :

https://doi.org/10.1017/9781108896139

Disponible sur ORBilu :

depuis le 07 décembre 2022

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174 (dont 3 Unilu)

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0 (dont 0 Unilu)

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Bibliographie

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