Online Learning and Adaptive Filters (Hardcover)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	November 2022
Authors:	Paulo S.R. Diniz • Marcello L. R. de Campos • Wallace A. Martins • Markus V.S. Lima • Jose A. Apolinario, Jr
Dimensions:	175 x 252 x 22mm (L x W x T)
Format:	Hardcover
Pages:	300
ISBN-13:	978-1-108-84212-9
Categories:	Books > Computing & IT > General theory of computing > Systems analysis & design Books > Computing & IT > Computer communications & networking > General Books > Computing & IT > Applications of computing > Signal processing Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory
LSN:	1-108-84212-7
Barcode:	9781108842129

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