“…These difficulties are usually overcome by augmenting or replacing the input vector with new variables, basis functions, which are transformations of the input variables, and then by using linear models in this new space of derived input features. Methods like sigmoidal feedforward neural networks (Bishop, 1995), projection pursuit learning (Friedman & Stuetzle, 1981), generalized additive models (Hastie & Tibshirani, 1990) and PolyMARS (Kooperberg, Bose, & Stone, 1997), and a hybrid of multivariate adaptive splines (Friedman, 1991;Tian-Shyug et al, 2006), specifically designed to solve classification problems, can be seen as different non-linear basis function models.…”