9781108842129-1108842127-Online Learning and Adaptive Filters

Online Learning and Adaptive Filters

ISBN-13: 9781108842129
ISBN-10: 1108842127
Edition: 1
Author: Paulo S. R. Diniz, Marcello L. R. de Campos, Wallace A. Martins, Markus V. S. Lima, Jose A. Apolinário Jr
Publication date: 2022
Publisher: Cambridge University Press
Format: Hardcover 300 pages
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Book details

ISBN-13: 9781108842129
ISBN-10: 1108842127
Edition: 1
Author: Paulo S. R. Diniz, Marcello L. R. de Campos, Wallace A. Martins, Markus V. S. Lima, Jose A. Apolinário Jr
Publication date: 2022
Publisher: Cambridge University Press
Format: Hardcover 300 pages

Summary

Online Learning and Adaptive Filters (ISBN-13: 9781108842129 and ISBN-10: 1108842127), written by authors Paulo S. R. Diniz, Marcello L. R. de Campos, Wallace A. Martins, Markus V. S. Lima, Jose A. Apolinário Jr, was published by Cambridge University Press in 2022. With an overall rating of 3.8 stars, it's a notable title among other books. You can easily purchase or rent Online Learning and Adaptive Filters (Hardcover) from BooksRun, along with many other new and used books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

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

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