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Online Learning and Adaptive Filters

Autor Paulo S. R. Diniz, Marcello L. R. de Campos, Wallace A. Martins, Markus V. S. Lima, Jose A. Apolinário, Jr
en Limba Engleză Hardback – 7 dec 2022
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.
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Specificații

ISBN-13: 9781108842129
ISBN-10: 1108842127
Pagini: 300
Dimensiuni: 170 x 244 x 16 mm
Greutate: 0.63 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:Cambridge, United Kingdom

Cuprins

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.

Notă biografică


Descriere

Discover up-to-date techniques and algorithms in this concise, intuitive text, with extensive solutions for challenging learning problems.