Digital Signal Processing with Kernel Methods (IEEE Press) - Hardcover

Rojo-Alvarez, Jose Luis; Martinez-Ramon, Manel; Munoz-Mari, Jordi; Camps-Valls, Gustau

Hardcover

ISBN 10: 1118611799 ISBN 13: 9781118611791

Verlag: Wiley-IEEE Press, 2018

Inhaltsangabe

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. 

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Reseña del editor

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.

Biografía del autor

Jose Luis Rojo-Alvarez, PhD, is a Full Professor in the Department of Signal Theory and Communications at the University Rey Juan Carlos, Spain. Manel Martinez-Ramon, PhD, is a Professor in the Department of Electrical and Computer Engineering at the University of New Mexico, Albuquerque, The United States of America. Jordi Munoz-Mari, PhD, is an Associate Professor in the Department of Electronics Engineering at the Universitat de Valencia, Spain. Gustau Camps-Valls, PhD, is an Associate Professor in the Department of Electronics Engineering at the Universitat de Valencia, Spain.

Details zur Ausgabe

• Verlag: Wiley-IEEE Press
• Erscheinungsdatum: 2018
• ISBN 10: 1118611799
• ISBN 13: 9781118611791
• Einband: Tapa dura
• Sprache: Englisch
• Auflage: 1
• Anzahl der Seiten: 672