Digital signal processing with kernel methods / (Record no. 74541)

000 -LEADER
fixed length control field 04516nam a2200613 i 4500
001 - CONTROL NUMBER
control field 8292908
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220712205958.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180227s2018 mau ob 001 eng d
019 ## -
-- 1017489244
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781118705810
-- electronic bk. : oBook
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- print
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- electronic bk.
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- electronic bk.
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- electronic bk. : oBook
082 04 - CLASSIFICATION NUMBER
Call Number 621.382/20285
100 1# - AUTHOR NAME
Author Rojo-�Alvarez, Jos�e Luis,
245 10 - TITLE STATEMENT
Title Digital signal processing with kernel methods /
250 ## - EDITION STATEMENT
Edition statement First edition.
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 PDF (672 pages).
505 0# - FORMATTED CONTENTS NOTE
Remark 2 From 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.
520 ## - SUMMARY, ETC.
Summary, etc 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.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
Subject Signal processing
General subdivision Digital techniques.
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
Subject Signal processing
General subdivision Digital techniques.
700 1# - AUTHOR 2
Author 2 Mart�inez-Ram�on, Manel,
700 1# - AUTHOR 2
Author 2 Mu�noz Mar�i, Jordi,
700 1# - AUTHOR 2
Author 2 Camps-Valls, Gustavo,
856 42 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=8292908
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Hoboken, New Jersey :
-- Wiley,
-- 2018.
264 #2 -
-- [Piscataqay, New Jersey] :
-- IEEE Xplore,
-- [2018]
336 ## -
-- text
-- rdacontent
337 ## -
-- electronic
-- isbdmedia
338 ## -
-- online resource
-- rdacarrier
588 0# -
-- Online resource; title from PDF title page (EBSCO, viewed January 10, 2018)

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