  We show theoretical similarities between the Least Squares Support Vector
Regression (LS-SVR) model with a Radial Basis Functions (RBF) kernel and
maximum a posteriori (MAP) inference on Bayesian 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 the existing correspondences. We empirically demonstrate our
result by performing computational experiments with standard regression
benchmarks. Our findings open a range of possibilities to improve LS-SVR by
borrowing strength from well-established developments in Bayesian methodology.

Machine Learning;Machine Learning