Online learning and adaptive filters / Paulo S.R. Diniz [and four others].
By: Diniz, Paulo Sergio Ramirez [author.]
.
Material type: 
BookPublisher: Cambridge, United Kingdom ; New York, NY, USA : Cambridge University Press, 2023Description: 1 online resource (xii, 253 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781108896139 (ebook).Subject(s): Adaptive signal processing -- Mathematics | Machine learning -- Mathematics | Signal processing -- Digital techniques -- Mathematics | Digital filters (Mathematics)Additional physical formats: Print version: : No titleDDC classification: 621.3822 Online resources: Click here to access online Summary: 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.
Title from publisher's bibliographic system (viewed on 24 Nov 2022).
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.
There are no comments for this item.