# LS-SVR as a Bayesian RBF network

arXiv: https://arxiv.org/abs/1905.00332 · HTML: https://mlsanity.com/paper/1905.00332/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2019.

## Built on by (most cited first, 1 of 1)

- [Probabilistic Approach for Road-Users Detection](https://mlsanity.com/paper/2112.01360/) (2021)

## Abstract (arXiv)

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.