A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Convex Support Vector Regression
Tekijät: Liao Zhiqiang, Dai Sheng, Kuosmanen Timo
Kustantaja: Elsevier
Julkaisuvuosi: 2023
Journal: European Journal of Operational Research
Lehden akronyymi: EUR J OPER RES
eISSN: 1872-6860
DOI: https://doi.org/10.1016/j.ejor.2023.05.009
Verkko-osoite: https://www.sciencedirect.com/science/article/pii/S0377221723003715
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/179441003
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.
Ladattava julkaisu This is an electronic reprint of the original article. |