# The Gaussian Radon Transform and Machine Learning

arXiv: https://arxiv.org/abs/1310.4794 · HTML: https://mlsanity.com/paper/1310.4794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2013.

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

- [Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning](https://mlsanity.com/paper/2412.04177/) (2024)

## Abstract (arXiv)

There has been growing recent interest in probabilistic interpretations of kernel-based methods as well as learning in Banach spaces. The absence of a useful Lebesgue measure on an infinite-dimensional reproducing kernel Hilbert space is a serious obstacle for such stochastic models. We propose an estimation model for the ridge regression problem within the framework of abstract Wiener spaces and show how the support vector machine solution to such problems can be interpreted in terms of the Gaussian Radon transform.