An explicit algorithm for training support vector machines
Davide Mattera, F. Palmieri, Simon Haykin · IEEE Signal Processing Letters · 1999
The support vector machine (SVM) constitutes one of the most powerful methods for constructing a mathematical model on the basis of a given number of training examples. SVM training requires that we solve a quadratic optimization problem; this step is usually performed by means of existing software packages. Such a black-box approach may be undesirable. In this paper we introduce a simple iterative algorithm for SVM training which compares well with some typical software packages, can be simply implemented, and has minimal memory requirements. It addresses the problem of regression estimation and utilizes ideas similar to those proposed by J. Platt (1998) for training binary SVM.