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Online Learning and Adaptive Filters [Kõva köide]

(University of Luxembourg), , (Universidade Federal do Rio de Janeiro), (Universidade Federal do Rio de Janeiro), (Universidade Federal do Rio de Janeiro)
  • Formaat: Hardback, 300 pages, kõrgus x laius x paksus: 251x175x19 mm, kaal: 630 g, Worked examples or Exercises
  • Ilmumisaeg: 08-Dec-2022
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1108842127
  • ISBN-13: 9781108842129
Teised raamatud teemal:
Online Learning and Adaptive FiltersSuurem pilt
  • Formaat: Hardback, 300 pages, kõrgus x laius x paksus: 251x175x19 mm, kaal: 630 g, Worked examples or Exercises
  • Ilmumisaeg: 08-Dec-2022
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1108842127
  • ISBN-13: 9781108842129
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781108842129.html
Märksõnad:
Learn to solve the unprecedented challenges facing Adaptive Signal Processing in this concise, intuitive text. Describing up-to-date techniques and algorithms in a condensed and unified way, this one-of-a-kind book allows you to implement solutions to practical problems and is an ideal resource for graduate students, researchers and professionals.

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.

Muu info

Discover up-to-date techniques and algorithms in this concise, intuitive text, with extensive solutions for challenging learning problems.
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.
Paulo S. R. Diniz is a Professor at the Universidade Federal do Rio de Janeiro and a Fellow of the IEEE and of EURASIP. He is a Senior Editor of the IEEE Open Journal of Signal Processing and is co-author of a CUP textbook on Digital Signal Processing. He is also a member of the National Academy of Engineering and the Brazilian Academy of Science. Marcello L. R. de Campos is a Professor at the Universidade Federal do Rio de Janeiro. He is a Senior Member of the IEEE and of the Brazilian Telecommunications Society, and member of the Brazilian Mathematical Society and of the Society for Industrial and Applied Mathematics. Wallace A. Martins is an Associate Professor at the Universidade Federal do Rio de Janeiro and a researcher with the University of Luxembourg. He is an Associate Editor for the IEEE Signal Processing Letters, and is currently a Senior Member of the IEEE and a member of the Brazilian Telecommunications Society. Markus V. S. Lima is an Associate Professor at the Universidade Federal do Rio de Janeiro and Chair of the IEEE Signal Processing Chapter in Rio de Janeiro. He is also a member of the Brazilian Telecommunications Society. José A. Apolinário, Jr. is an Associate Professor at the Military Institute of Engineering. He is a Senior Member of the IEEE and the Brazilian Society of Telecommunications.