Please use this identifier to cite or link to this item:
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.
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
ISSN: 10459227
DOI: 10.1109/TNN.2011.2106219
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Dec 11, 2018


checked on Dec 11, 2018

Page view(s)

checked on Dec 8, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.