Online Learning and Adaptive Filters
Paulo S. R. Diniz, Federal University of Rio de Janiero;
Marcello L. R. de Campos, Federal University of Rio de Janiero;
Wallace A. Martins, Federal University of Rio de Janiero;
Markus V. S. Lima, Federal University of Rio de Janiero;
José A. Apolinário, Military Institute of Engineering
Cambridge University Press, 2022
ISBN: 9781108842129;
Language: English
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 in Online Learning and Adaptive Filters 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.
MATLAB is used to solve examples throughout the book. Statistics and Machine Learning Toolbox, Signal Processing Toolbox, and Communications Toolbox are briefly introduced.
