# LS-SVR as a Bayesian RBF network

## TL;DR

This paper establishes a formal connection between LS-SVR with RBF kernels and Bayesian RBF networks, enabling potential improvements by integrating Bayesian methods into LS-SVR.

## Contribution

It explicitly states the theoretical similarities between LS-SVR and Bayesian RBF networks, providing a formal basis for future methodological enhancements.

## Key findings

- Confirmed the theoretical correspondence through experiments
- Demonstrated potential for improving LS-SVR with Bayesian techniques
- Validated the approach on standard regression benchmarks

## Abstract

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.