Digital signal processing with kernel methods
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…
Read More ↓Title Statements
- Full title :Digital signal processing with kernel methods / José Luis Rojo-Álvarez (Department of Signal Theory and Communications, University Rey Juan Carlos Fuenlabrada (Madrid) and Center for Computational Simulation Universidad Politécnica de Madrid, Spain), Manel Martínez-Ramón (Department of Electrical and Computer Engineering, the University of New Mexico Albuquerque, New Mexico, USA), Jordi Muñoz-Marí (Department of Electronics Engineering, Universitat de València Paterna (València), Spain), Gustau Camps-Valls (Department of Electronics Engineering, Universitat de València Paterna (València), Spain)
Summary
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
Read More ↓Edition Statements
- Publication Date:2018
- Edition:First edition
Publication Statements
- Publisher(s):Wiley
- Place of Publication:Hoboken, NJ
- Language:eng
- Publication Date:2018
The Physical Object
- Description:xxvi, 639 Seiten : Illustrationen, Diagramme
Edition Identifiers
- ISBN:9781118611791, 1118611799, 9781118705827, 1118705823, 9781118705834, 1118705831
- oclc:on1022835243
- LC Classification:TK5102.9
- Dewey Decimal:621.382/20285
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