✦ LIBER ✦
Support vector learning with quadratic programming and adaptive step size barrier-projection
✍ Scribed by K.N. To; C.C. Lim; K.L. Teo; M.J. Liebelt
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 672 KB
- Volume
- 47
- Category
- Article
- ISSN
- 0362-546X
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✦ Synopsis
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 step-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 illconditioned Hessian.