000 04516nam a2200613 i 4500
001 8292908
003 IEEE
005 20220712205958.0
006 m o d
007 cr |n|||||||||
008 180227s2018 mau ob 001 eng d
010 _z 2017044705 (print)
019 _a1017489244
020 _a9781118705810
_qelectronic bk. : oBook
020 _z9781118611791
_qprint
020 _z9781118705827
_qelectronic bk.
020 _z1118705823
_qelectronic bk.
020 _z1118705815
_qelectronic bk. : oBook
020 _z9781118705834
020 _z1118705831
024 7 _a10.1002/9781118705810
_2doi
035 _a(CaBNVSL)mat08292908
035 _a(IDAMS)0b00006486c75dcd
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aTK5102.9
082 0 4 _a621.382/20285
_223
100 1 _aRojo-�Alvarez, Jos�e Luis,
_d1972-
_eauthor.
_929253
245 1 0 _aDigital signal processing with kernel methods /
_cby Dr. Jos�e Luis Rojo-�Alvarez, Dr. Manel Mart�inez-Ram�on, Dr. Jordi Mu�noz-Mar�i, Dr. Gustau Camps-Valls.
250 _aFirst edition.
264 1 _aHoboken, New Jersey :
_bWiley,
_c2018.
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2018]
300 _a1 PDF (672 pages).
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
504 _aIncludes bibliographical references and index.
505 0 _aFrom signal processing to machine learning -- Introduction to digital signal processing -- Signal processing models -- Kernel functions and reproducing kernel hilbert spaces -- A SVM signal estimation framework -- Reproducing kernel hilbert space models for signal processing -- Dual signal models for signal processing -- Advances in kernel regression and function approximation -- Adaptive kernel learning for signal processing -- SVM and kernel classification algorithms -- Clustering and anomaly detection with kernels -- Kernel feature extraction in signal processing.
506 _aRestricted to subscribers or individual electronic text purchasers.
520 _a A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. . Presents the necessary basic ideas from both digital signal processing and machine learning concepts. Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing. Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
588 0 _aOnline resource; title from PDF title page (EBSCO, viewed January 10, 2018)
650 0 _aSignal processing
_xDigital techniques.
_93369
650 7 _aSignal processing
_xDigital techniques.
_2fast
_93369
655 4 _aElectronic books.
_93294
700 1 _aMart�inez-Ram�on, Manel,
_d1968-
_eauthor.
_929254
700 1 _aMu�noz Mar�i, Jordi,
_eauthor.
_929255
700 1 _aCamps-Valls, Gustavo,
_d1972-
_eauthor.
_929256
710 2 _aIEEE Xplore (Online Service),
_edistributor.
_929257
710 2 _aWiley,
_epublisher.
_929258
776 0 8 _iPrint version:
_z9781118611791
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=8292908
942 _cEBK
999 _c74541
_d74541