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Digital signal processing with kernel methods /
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. --
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
Wiley,
2017.
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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.