An explicit algorithm for training support vector machines

The support vector machine (SVM) constitutes one of the most powerful metho ds for constructing a mathematical model on the basis of a given number of training examples. SVM training requires that we solve a quadratic optimiza tion problem; this step is usually performed by means of existing software packages, Such a black-box approach may be undesirable. In this paper we in troduce a simple iterative algorithm for SVM training which compares well w ith some typical software packages, can be simply implemented, and has mini mal memory requirements, It addresses the problem of regression estimation and utilizes ideas similar to those proposed in [1] for trraining binary SV M.