Support vector learning with quadratic programming and adaptive step size barrier-projection
2001
To, K. | Lim, C. | Teo, K. | Liebelt, M.
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.
Show more [+] Less [-]K. N. To, C. C. Lim, K. L. Teo and M. J. Liebelt
Show more [+] Less [-]http://www.elsevier.com/wps/find/journaldescription.cws_home/239/description#description
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