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

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

Part III Classification, Detection, and Feature Extraction

10 Support Vector Machine and Kernel Classification Algorithms
11 Clustering and Anomaly Detection with Kernels
12 Kernel Feature Extraction in Signal Processing

References
Index