LEADER 04007nam 2200577 450 001 9910810934503321 005 20240219165728.0 010 $a1-118-70583-1 010 $a1-118-70582-3 010 $a1-118-70581-5 024 7 $a10.1002/9781118705810 035 $a(CKB)4330000000006847 035 $a(CaBNVSL)mat08292908 035 $a(IDAMS)0b00006486c75dcd 035 $a(IEEE)8292908 035 $a(Au-PeEL)EBL5210130 035 $a(CaPaEBR)ebr11491027 035 $a(PPN)249919567 035 $a(CaSebORM)9781118611791 035 $a(MiAaPQ)EBC5210130 035 $a(OCoLC)1004376608 035 $a(EXLCZ)994330000000006847 100 $a20180227d2018 uy 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aDigital signal processing with kernel methods /$fby Dr. Jose? Luis Rojo-A?lvarez, Dr. Manel Marti?nez-Ramo?n, Dr. Jordi Mun?oz-Mari?, Dr. Gustau Camps-Valls 205 $aFirst edition. 210 1$aHoboken, New Jersey :$cWiley,$d2018. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2018] 215 $a1 online resource (668 pages) $cillustrations 311 $a1-118-61179-9 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aSignal processing$xDigital techniques 615 0$aSignal processing$xDigital techniques. 676 $a621.38220285 700 $aRojo-A?lvarez$b Jose? Luis$f1972-$01658377 702 $aMarti?nez-Ramo?n$b Manel$f1968- 702 $aMun?oz Mari?$b Jordi 702 $aCamps-Valls$b Gustavo$f1972- 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910810934503321 996 $aDigital signal processing with kernel methods$94012355 997 $aUNINA