Online Learning and Adaptive Filters

Online Learning and Adaptive Filters

 
Publisher: Cambridge University Press
Date of Publication:
 
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Product details:

ISBN13:9781108842129
ISBN10:1108842127
Binding:Hardback
No. of pages:300 pages
Size:251x175x19 mm
Weight:630 g
Language:English
577
Category:
Short description:

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

Long 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.
Table of Contents:
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.