Digital signal processing with kernel methods / by Dr. José Luis Rojo-Álvarez, Dr. Manel Martínez-Ramón, Dr. Jordi Muñoz-Marí, Dr. Gustau Camps-Valls.
By: Rojo-Álvarez, José Luis [author.]
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Contributor(s): Martínez-Ramón, Manel [author.]
| Muñoz Marí, Jordi [author.]
| Camps-Valls, Gustavo [author.]
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Material type: 



Online resource; title from PDF title page (EBSCO, viewed January 10, 2018).
Includes bibliographical references and index.
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.
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. -- Edited summary from book.
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