Inhoudsopgave:
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\u003cp\u003e\u003cb\u003eA realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003ci\u003eDigital Signal Processing with Kernel Methods\u003c/i\u003e 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.\u003c/p\u003e \u003cp\u003e\u003ci\u003eDigital Signal Processing with Kernel Methods \u003c/i\u003eprovides 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. \u003c/p\u003e \u003cul\u003e \u003cli\u003ePresents the necessary basic ideas from both digital signal processing and machine learning concepts\u003c/li\u003e \u003cli\u003eReviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing\u003c/li\u003e \u003cli\u003eSurveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing\u003c/li\u003e \u003c/ul\u003e \u003cp\u003eAn excellent book for signal processing researchers and practitioners, \u003ci\u003eDigital Signal Processing with Kernel Methods \u003c/i\u003ewill also appeal to those involved in machine learning and pattern recognition. \u003c/p\u003e |
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