This unique text offers a unified overview of the topic, providing detailed examples of applications, as well as covering the fundamental underlying theory, including chapters on interpolation and approximation, Cholesky and Schur ...
In this work. we propose a unified framework called Kalman filter based Radial Basis Functions (KF-RBF) for online functional prediction based on the Radial Basis Functions and the Kalman Filter.
... kernel feature analysis Principal components regression Persistently exciting Partial least squares Proper ... basis function Radial basis function neural network Regularized kernel discriminant analysis Reproducing kernel Hilbert space ...
The book is of interest to a wide audience of pure and applied mathematicians, electrical engineers and theoretical physicists.
... network , 45 interval matrices , 202 K.U.Leuven time series competition , 241 Kalman gain matrix , 38 Karhunen - Loève transformation , 226 Kuhn - Tucker conditions , 60 Lagrange function ... basis , 123 overtraining , 11 P - dimension ...
... Radial Basis Function Network Reproducing Kernel Hilbert Space Recursive Information Potential Recursive Least Squares Receiver Operating Characteristics Resilient Back Propagation Algorithm Synthetic Aperture Radar Scaled Conjugate ...
... radial basis function ( RBF ) network with a growing structure , where centers are placed at the projected samples and the weights are directly related to ... Reproducing Kernel Hilbert Spaces 525 30.2 Reproducing Kernel Hilbert Spaces.
The novel viewpoint is that reproducing kernel Hilbert space theory studies extrinsic geometry, associating with each geometric configuration a canonical overdetermined coordinate system.
... Radial Basis Function Networks in Reproducing Kernel Hilbert Space (再生核ヒルベルト空間における動径基底関数ネットワーク)論文調查委員(主査) (副查)九州大学教授和田" " 1 和二清保俊川邊武俊論文内容の要旨 Most real - world systems exhibit ...