论文标题
凸支持向量回归
Convex Support Vector Regression
论文作者
论文摘要
在经济学，金融，运营研究，机器学习和统计中，受到凸度或凹面约束的非参数回归越来越受欢迎。但是，基于最小二乘损耗函数的常规凸回归通常会遭受过度拟合和异常值的影响。本文建议通过引入凸支持向量回归（CSVR）方法来解决这两个问题，该方法有效地结合了凸回归的关键要素和支持向量回归。数值实验证明了CSVR在预测准确性和鲁棒性方面的性能，与其他最新方法相比，它比较有利。
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.
