Digital signal processing with kernel methods
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 s...
| Otros Autores: | , , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley
2018.
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631591706719 |
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.
