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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, 1972- [author.].
Contributor(s): Martínez-Ramón, Manel, 1968- [author.] | Muñoz Marí, Jordi [author.] | Camps-Valls, Gustavo, 1972- [author.].
Material type: materialTypeLabelBookPublisher: Hoboken, NJ : Wiley, 2018Copyright date: ©2018Edition: First edition.Description: 1 online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781118705827; 1118705823; 9781118705810; 1118705815.Subject(s): Signal processing -- Digital techniques | TECHNOLOGY & ENGINEERING -- Mechanical | Signal processing -- Digital techniquesGenre/Form: Electronic books.Additional physical formats: Print version:: Digital signal processing with kernel methods.DDC classification: 621.382/20285 Online resources: Wiley Online Library
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.
Summary: 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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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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