Paulo S. R. Diniz; Marcello L. R. de Campos; Wallace A. Martins; Markus V. S. Lima; Jr Jose A. Apolinário Cambridge University Press (2022) Saatavuus: Tilaustuote Kovakantinen kirja
Frank S. de Boer (ed.); Marcello M. Bonsangue (ed.); Susanne Graf (ed.); Willem-Paul de Roever (ed.) Springer (2007) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Frank S. de Boer (ed.); Marcello M. Bonsangue (ed.); Susanne Graf (ed.); Willem-Paul de Roever (ed.) Springer (2004) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Frank S. de Boer (ed.); Marcello M. Bonsangue (ed.); Susanne Graf (ed.); Willem-Paul de Roever (ed.) Springer (2006) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Frank S. de Boer (ed.); Marcello M. Bonsangue (ed.); Susanne Graf (ed.); Willem-Paul de Roever (ed.) Springer (2005) Saatavuus: Tilaustuote Pehmeäkantinen kirja
Paulo Mazzoncini de Azevedo-Marques; Arianna Mencattini; Marcello Salmeri; Rangaraj M. Rangayyan Taylor & Francis Inc (2017) Saatavuus: Tilaustuote Kovakantinen kirja
Paulo Mazzoncini de Azevedo-Marques; Arianna Mencattini; Marcello Salmeri; Rangaraj M. Rangayyan Taylor & Francis Ltd (2019) Saatavuus: Tilaustuote 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.
