A GENERALISED LS-SVM

摘要

The Least-Squares Support Vector Machine (LS-SVM) solution can be obtained by solving a linear equation set. This allows us to address the problems in a linear fashion. In this paper we present a generalised view of the Least-Squares Support Vector Regression (SVR), which enables us to develop new formulations and algorithms to this regression technique. The modifications are based on manipulating the linear equation set, which embodies all information about the regression in the learning process. These modifications simplify the formulations, speed up' the calculations and/or provide better results.