9783838313085 - Nonlinear Signal Processing Based on Reproducing Kernel Hilbert Space: Concepts,Methods and Experiments di Xu, Jianwu (6 risultati)

Taschenbuch. Condizione: Neu. Nonlinear Signal Processing Based on Reproducing Kernel Hilbert Space | Concepts,Methods and Experiments | Jianwu Xu | Taschenbuch | 164 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838313085 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Over the last few decades, there has been much research on reproducing kernel Hilbert space (RKHS) for machine learning. This monograph applies RKHS for nonlinear signal processing. It proposes a new statistical descriptor, called correntropy, to characterize the higher order statistical information and nonlinearity intrinsic to random processes. Correntropy and centered correntropy functions can be formulated as 'generalized' correlation and covariance functions on nonlinearly transformed random signals via the data independent kernel functions. Those nonlinearly transformed signals appear on the sphere in the reproducing kernel Hilbert space induced by the kernel functions if isotropic kernel functions are used. The book offers new insights into the application of RKHS in nonlinear signal processing. Graduate students, professionals and researchers who are working on nonlinear signal processing and machine learning will find insightful information from the book. 164 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Xu JianwuJianwu Xu received Ph.D. in Electrical and Computer Engineeringfrom the University of Florida. Currently, He is a Post-DoctoralScholar at the University of Chicago working on Computer-AidedDiagnosis.Over the last few dec.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Over the last few decades, there has been much research on reproducing kernel Hilbert space (RKHS) for machine learning. This monograph applies RKHS for nonlinear signal processing. It proposes a new statistical descriptor, called correntropy, to characterize the higher order statistical information and nonlinearity intrinsic to random processes. Correntropy and centered correntropy functions can be formulated as 'generalized' correlation and covariance functions on nonlinearly transformed random signals via the data independent kernel functions. Those nonlinearly transformed signals appear on the sphere in the reproducing kernel Hilbert space induced by the kernel functions if isotropic kernel functions are used. The book offers new insights into the application of RKHS in nonlinear signal processing. Graduate students, professionals and researchers who are working on nonlinear signal processing and machine learning will find insightful information from the book.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 164 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Over the last few decades, there has been much research on reproducing kernel Hilbert space (RKHS) for machine learning. This monograph applies RKHS for nonlinear signal processing. It proposes a new statistical descriptor, called correntropy, to characterize the higher order statistical information and nonlinearity intrinsic to random processes. Correntropy and centered correntropy functions can be formulated as 'generalized' correlation and covariance functions on nonlinearly transformed random signals via the data independent kernel functions. Those nonlinearly transformed signals appear on the sphere in the reproducing kernel Hilbert space induced by the kernel functions if isotropic kernel functions are used. The book offers new insights into the application of RKHS in nonlinear signal processing. Graduate students, professionals and researchers who are working on nonlinear signal processing and machine learning will find insightful information from the book.