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Learning a function and its derivative forcing the support vector expansion
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DOI：10.1109/LSP.2004.840841.png)
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摘要
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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.
Keyword:
function approximation
IRWLS
support vectors
SVM
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