Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2011.2106219
Title: Determination of global minima of some common validation functions in support vector machine
Authors: Yang, J.-B.
Ong, C.-J. 
Keywords: Model selection
regularization path
support vector machine
tuning of regularization parameter
Issue Date: Apr-2011
Citation: Yang, J.-B., Ong, C.-J. (2011-04). Determination of global minima of some common validation functions in support vector machine. IEEE Transactions on Neural Networks 22 (4) : 654-659. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2011.2106219
Abstract: 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. © 2011 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/59884
ISSN: 10459227
DOI: 10.1109/TNN.2011.2106219
Appears in Collections:Staff Publications

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