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https://hdl.handle.net/2440/2461
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Type: | Journal article |
Title: | Support vector learning with quadratic programming and adaptive step size barrier-projection |
Author: | To, K. Lim, C. Teo, K. Liebelt, M. |
Citation: | Nonlinear Analysis Theory Methods and Applications, 2001; 47(8 Part 8 Special Issue SI):5623-5633 |
Publisher: | Pergamon-Elsevier Science Ltd |
Issue Date: | 2001 |
ISSN: | 0362-546X |
Statement of Responsibility: | K. N. To, C. C. Lim, K. L. Teo and M. J. Liebelt |
Abstract: | We consider a support vector machine training problem involving a quadratic objective function with a single linear equality constraint and a box constraint. Using quadratic surjective space transformation to create a barrier for the gradient method, an iterative support vector learning algorithm is derived. We further derive a stable steepest descent method to find the stop-size in order to reduce the number of iterations to reach the optimal solution. This method offers speed improvement over the fixed step-size gradient method, in particular for QP problems with ill-conditioned Hessian. |
Keywords: | Support vector machines quadratic programming barrier projection method |
DOI: | 10.1016/S0362-546X(01)00664-2 |
Description (link): | http://www.elsevier.com/wps/find/journaldescription.cws_home/239/description#description |
Published version: | http://dx.doi.org/10.1016/s0362-546x(01)00664-2 |
Appears in Collections: | Aurora harvest 2 Electrical and Electronic Engineering publications Environment Institute publications |
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