Training Semiparametric Support Vector Machines

The semiparametric Support Vector Machine (SVM) has recently been introduced as a generalization of the classical SVM to the case in which some a priori knowledge about the considered problem is available. Semiparametric SVM training requires that we solve an optimization problem very similar (it only imposes a larger number of equality constraints) to that to be solved for classical SVM training. In both cases training is usually performed by means of existing software packages. Since this black-box approach may be undesirable, with reference to the classical SVM, some simple and explicit algorithms, difficult to extend to the semiparametric case, have recently been proposed. In this paper we introduce a simple iterative algorithm for semiparametric SVM training which compares well with some typical software packages, can be simply implemented and has minimal memory requirements.