Paulo S. R. Diniz; Marcello L. R. de Campos; Wallace A. Martins; Markus V. S. Lima; Jr Jose A. Apolinário Cambridge University Press (2022) Kovakantinen kirja
Jack A. Hyman; Luca Massaron; Paul McFedries; John Paul Mueller; Jonathan Reichental; Joseph Schmuller; Alan R. Simon; Ta John Wiley & Sons Inc (2024) Pehmeäkantinen kirja
Paul R. Krugman; Maurice Obstfeld; David A. Buchanan; Andrzej Huczynski; Michael P. Todaro; Stephen C. Smith; Bill Neale Pearson Education Limited (2006)
Antonio Pedro Aguiar (ed.); Paula Rocha Malonek (ed.); Vítor Hugo Pinto (ed.); Fernando A. C. C. Fontes (ed.); Chertovskih Springer (2025) Kovakantinen kirja
David R. Holmes Jr.; Robert L. Frye; Paul A. Friedman; Donald J Hagler; Thomas M. Munger; Erik L. Ritman Springer Nature Switzerland AG (2021) Kovakantinen kirja
Pauline Moore; Jennifer T Leschitz; Brian A Jackson; Catherine H Augustine; Andrea Phillips; Elizabeth D Steiner RAND Corporation (2022) Pehmeäkantinen kirja
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.
