Determination of global minima of some common validation functions in support vector machine

JB Yang, CJ Ong - IEEE transactions on neural networks, 2011 - ieeexplore.ieee.org
IEEE transactions on neural networks, 2011ieeexplore.ieee.org
Tuning of the regularization parameter C is a well-known process in the implementation of a
support vector machine (SVM) classifier. Such a tuning process uses an appropriate
validation function whose value, evaluated over a validation set, has to be optimized for the
determination of the optimal C. Unfortunately, most common validation functions are not
smooth functions of C. This brief presents a method for obtaining the global optimal solution
of these non-smooth validation functions. The method is guaranteed to find the global …
Tuning of the regularization parameter C is a well-known process in the implementation of a support vector machine (SVM) classifier. Such a tuning process uses an appropriate validation function whose value, evaluated over a validation set, has to be optimized for the determination of the optimal C . Unfortunately, most common validation functions are not smooth functions of C . This brief presents a method for obtaining the global optimal solution of these non-smooth validation functions. The method is guaranteed to find the global optimum and relies on the regularization solution path of SVM over a range of C values. When the solution path is available, the computation needed is minimal.
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