Convex support vector regression
针对传统凸回归易过拟合和受异常值影响的问题，提出了凸支持向量回归方法，结合凸回归与支持向量回归的优点，数值实验表明其在预测精度和稳健性上优于现有方法。
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.