# Convex Support Vector Regression

arXiv: https://arxiv.org/abs/2209.12538 · HTML: https://mlsanity.com/paper/2209.12538/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2022.

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

- [Identifiable Convex-Concave Regression via Sub-gradient Regularised Least Squares](https://mlsanity.com/paper/2506.18078/) (2025)

## Abstract (arXiv)

Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers. This paper proposes to address these two issues by introducing the convex support vector regression (CSVR) method, which effectively combines the key elements of convex regression and support vector regression. Numerical experiments demonstrate the performance of CSVR in prediction accuracy and robustness that compares favorably with other state-of-the-art methods.