LS-SVR as a Bayesian RBF network

We show the theoretical equivalence between the Least Squares Support Vector Regression (LS-SVR) model and maximum a posteriori (MAP) inference on Bayesian Radial Basis Functions (RBF) networks with a specific Gaussian prior on the regression weights. Although previous works have pointed out similar expressions between those learning approaches, we explicit and formally state such correspondence. We empirically demonstrate our result by performing computational experiments with standard regression benchmarks. Our findings open a range of possibilities to improve LS-SVR borrowing strength from well-established developments in Bayesian methodology.