Convex Support Vector Regression

2022/09/26 by Zhiqiang Liao, Liao, Zhiqiang, Sheng Dai +3 · 2 citations
Computer Science · Mathematics · #Computational Drug Discovery Methods #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2209.12538

openalex publication_date 2022/09/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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• Sparse Convex Quantile Regression: A Generalized Benders Decomposition Approach
• Identifiable Convex-Concave Regression via Sub-gradient Regularised Least Squares

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