000			04436nam a2200601 i 4500
001			8292908
003			IEEE
005			20191218152133.0
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.
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.
650		7	_aSignal processing _xDigital techniques. _2fast
655		4	_aElectronic books.
700	1		_aMart�inez-Ram�on, Manel, _d1968- _eauthor.
700	1		_aMu�noz Mar�i, Jordi, _eauthor.
700	1		_aCamps-Valls, Gustavo, _d1972- _eauthor.
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aWiley, _epublisher.
776	0	8	_iPrint version: _z9781118611791
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=8292908
999			_c43044 _d43044