Part III: Classification, Detection, and Feature Extraction
by Gustau Camps-Valls, Jordi Muñoz-Marí, Manel Martínez-Ramón, José Luis Rojo-Álvar
Digital Signal Processing with Kernel Methods
- Cover
- Title Page
- About the Authors
- Preface
- Acknowledgements
- List of Abbreviations
- Part I: Fundamentals and Basic Elements
- 1 From Signal Processing to Machine Learning
- 2 Introduction to Digital Signal Processing
- 3 Signal Processing Models
- 4 Kernel Functions and Reproducing Kernel Hilbert Spaces
- 4.1 Introduction
- 4.2 Kernel Functions and Mappings
- 4.3 Kernel Properties
- 4.4 Constructing Kernel Functions
- 4.5 Complex Reproducing Kernel in Hilbert Spaces
- 4.6 Support Vector Machine Elements for Regression and Estimation
- 4.7 Tutorials and Application Examples
- 4.8 Concluding Remarks
- 4.9 Questions and Problems
- Part II: Function Approximation and Adaptive Filtering
- 5 A Support Vector Machine Signal Estimation Framework
- 6 Reproducing Kernel Hilbert Space Models for Signal Processing
- 7 Dual Signal Models for Signal Processing
- 8 Advances in Kernel Regression and Function Approximation
- 9 Adaptive Kernel Learning for Signal Processing
- 9.1 Introduction
- 9.2 Linear Adaptive Filtering
- 9.3 Kernel Adaptive Filtering
- 9.4 Kernel Least Mean Squares
- 9.5 Kernel Recursive Least Squares
- 9.6 Explicit Recursivity for Adaptive Kernel Models
- 9.7 Online Sparsification with Kernels
- 9.8 Probabilistic Approaches to Kernel Adaptive Filtering
- 9.9 Further Reading
- 9.10 Tutorials and Application Examples
- 9.11 Questions and Problems
- Part III: Classification, Detection, and Feature Extraction
- References
- Index
- End User License Agreement
-
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