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Convex Support Vector Regression




TekijätLiao Zhiqiang, Dai Sheng, Kuosmanen Timo

KustantajaElsevier

Julkaisuvuosi2023

JournalEuropean Journal of Operational Research

Lehden akronyymiEUR J OPER RES

eISSN1872-6860

DOIhttps://doi.org/10.1016/j.ejor.2023.05.009

Verkko-osoitehttps://www.sciencedirect.com/science/article/pii/S0377221723003715

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/179441003


Tiivistelmä

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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Last updated on 2025-27-03 at 22:03