Enhancing Efficiency and Robustness in Support Vector Regression with HawkEye Loss. (arXiv:2401.16785v1 [cs.LG])

Support vector regression (SVR) has garnered significant popularity over the
past two decades owing to its wide range of applications across various fields.
Despite its versatility, SVR encounters challenges when confronted with
outliers and noise, primarily due to the use of the $\varepsilon$-insensitive
loss function. To address this limitation, SVR with bounded loss functions has
emerged as an appealing alternative, offering enhanced generalization
performance and robustness. Notably, recent developments focus on designing
bounded loss functions with smooth characteristics, facilitating the …

applications arxiv challenges cs.lg efficiency fields function loss noise outliers regression robustness support vector