Multi-dimensional Function Approximation and Regression Estimation
نویسندگان
چکیده
In this communication, we generalize the Support Vector Machines (SVM) for regression estimation and function approximation to multi-dimensional problems. We propose a multi-dimensional Support Vector Regressor (MSVR) that uses a cost function with a hyperspherical insensitive zone, capable of obtaining better predictions than using an SVM independently for each dimension. The resolution of the MSVR is achieved by an iterative procedure over the Karush-KuhnTucker conditions. The proposed algorithm is illustrated by computers experiments.
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تاریخ انتشار 2002