Digital signal processing with kernel methods / by Dr. José Luis Rojo-Álvarez, Dr. Manel Martínez-Ramón, Dr. Jordi Muñoz-Marí, Dr. Gustau Camps-Valls.
Offering example applications and detailed benchmarking experiments with real and synthetic datasets throughout, this book provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. --
Saved in:
| Online Access: |
Full Text (via Wiley) |
|---|---|
| Main Authors: | , , , |
| Format: | eBook |
| Language: | English |
| Published: |
Hoboken, NJ :
Wiley,
2017.
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| Edition: | First edition. |
| Subjects: |
Table of Contents:
- From signal processing to machine learning
- Introduction to digital signal processing
- Signal processing models
- Kernel functions and reproducing kernel hilbert spaces
- A SVM signal estimation framework
- Reproducing kernel hilbert space models for signal processing
- Dual signal models for signal processing
- Advances in kernel regression and function approximation
- Adaptive kernel learning for signal processing
- SVM and kernel classification algorithms
- Clustering and anomaly detection with kernels
- Kernel feature extraction in signal processing.