eBook - ePub
Digital Signal Processing with Kernel Methods
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Digital Signal Processing with Kernel Methods
About this book
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: http://github.com/DSPKM
• 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.
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: http://github.com/DSPKM
• 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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Yes, you can access Digital Signal Processing with Kernel Methods by Jose Luis Rojo-Alvarez,Manel Martinez-Ramon,Jordi Munoz-Mari,Gustau Camps-Valls in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Signals & Signal Processing. We have over one million books available in our catalogue for you to explore.
Information
Edition
1Subtopic
Signals & Signal ProcessingTable of contents
- Cover
- Title Page
- Table of Contents
- About the Authors
- Preface
- Acknowledgements
- List of Abbreviations
- Part I: Fundamentals and Basic Elements
- Part II: Function Approximation and Adaptive Filtering
- Part III: Classification, Detection, and Feature Extraction
- References
- Index
- End User License Agreement