Boosting Orthogonal Least Squares Regression

A comparison between the support vector machine regression (SVR) and the orthogonal least square (OLS) forward selection regression is given by an example. The disadvantage of SVR is shown and analyzed. A new algorithm by using OLS method to select regressors (support vectors) and boosting method to train the regressors’ weight is proposed. This algorithm can give a small regression error when a very sparse system model is required. When a detailed model is required, the resulted train set error model and the test set error model may look very similar.