• Online Learning and Adaptive Filters

Online Learning and Adaptive Filters

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Est. Date: Jan 5, 2026

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.

  • Author(s): Paulo S. R. Diniz, Marcello L. R. de Campos, Wallace A. Martins, Markus V. S. Lima, Jose A. Apolinário, Jr
  • Publisher: Cambridge University Press
  • Language: en
  • Pages: 300
  • Binding: Hardcover
  • Edition: 1
  • Published: 2022-12-08
  • Dimensions: Height: 9.61 Inches, Length: 6.69 Inches, Weight: 1.3889122506 Pounds, Width: 0.63 Inches
  • Estimated Delivery: Jan 5, 2026
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