000 02410nam a2200361 i 4500
001 CR9781108896139
003 UkCbUP
005 20240730160742.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 200108s2023||||enk o ||1 0|eng|d
020 _a9781108896139 (ebook)
020 _z9781108842129 (hardback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 0 0 _aTK5102.9
_b.D634 2023
082 0 0 _a621.3822
_223/eng/20220826
100 1 _aDiniz, Paulo Sergio Ramirez,
_d1956-
_eauthor.
_974424
245 1 0 _aOnline learning and adaptive filters /
_cPaulo S.R. Diniz [and four others].
264 1 _aCambridge, United Kingdom ; New York, NY, USA :
_bCambridge University Press,
_c2023.
300 _a1 online resource (xii, 253 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _aTitle from publisher's bibliographic system (viewed on 24 Nov 2022).
520 _aLearn 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.
650 0 _aAdaptive signal processing
_xMathematics.
_918409
650 0 _aMachine learning
_xMathematics.
_94005
650 0 _aSignal processing
_xDigital techniques
_xMathematics.
_97229
650 0 _aDigital filters (Mathematics)
_921715
776 0 8 _iPrint version:
_z9781108842129
856 4 0 _uhttps://doi.org/10.1017/9781108896139
942 _cEBK
999 _c84123
_d84123