Learning a function and its derivative forcing the support vector expansion
M Lázaro, F Pérez-Cruz… - IEEE Signal Processing …, 2005 - ieeexplore.ieee.org
IEEE Signal Processing Letters, 2005•ieeexplore.ieee.org
In this paper, a new method for the simultaneous learning of a function and its derivative is
presented. The method, setting out the problem inside of the Support Vector Machine (SVM)
framework, relies on the kernel-based Support Vector expansion. The resultant optimization
problem is solved by a computationally efficient Iterative Re-Weighted Least Squares
(IRWLS) algorithm.
presented. The method, setting out the problem inside of the Support Vector Machine (SVM)
framework, relies on the kernel-based Support Vector expansion. The resultant optimization
problem is solved by a computationally efficient Iterative Re-Weighted Least Squares
(IRWLS) algorithm.
In this paper, a new method for the simultaneous learning of a function and its derivative is presented. The method, setting out the problem inside of the Support Vector Machine (SVM) framework, relies on the kernel-based Support Vector expansion. The resultant optimization problem is solved by a computationally efficient Iterative Re-Weighted Least Squares (IRWLS) algorithm.
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