Convex Support Vector Regression
Liao Zhiqiang; Dai Sheng; Kuosmanen Timo
Convex Support Vector Regression
Liao Zhiqiang
Dai Sheng
Kuosmanen Timo
Cornell University
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2022102463159
https://urn.fi/URN:NBN:fi-fe2022102463159
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.
Kokoelmat
- Rinnakkaistallenteet [19207]