Online Learning and Adaptive Filters

Paulo S. R. Diniz; Marcello L. R. de Campos; Wallace A. Martins; Markus V. S. Lima; Jose A. Apolinário, Jr

doi:10.1017/9781108896139

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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Online Learning and Adaptive Filters

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Contents

Frontmatter
pp i-iv

Contents
pp v-viii

Preface
pp ix-xii

Acknowledgements
pp xiii-xiv

Introduction
pp 1-15

2 - Adaptive Filtering for Sparse Models
pp 16-59

3 - Kernel-Based Adaptive Filtering
pp 60-108

4 - Distributed Adaptive Filters
pp 109-151

5 - Adaptive Beamforming
pp 152-197

6 - Adaptive Filtering on Graphs
pp 198-249

Index
pp 250-253

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