<i>v</i>-SVR with Imprecise Observations

Support vector regression (SVR) has been widely used in academia and industry with excellent performance. Crisp data always be trained by classic SVR and its varieties. However, classic SVR is feeble if data are imprecise or low-quality. Hence, the uncertainty theory emerged as the times require, which can process the imprecise observations well. In this study, a novel SVR model be introduced into uncertainty theory, termed v-SVR with imprecise observations, designed to handle imprecise or low-quality data. Unlike the conventional [Formula: see text]-SVR with imprecise observations approach, v-SVR offers an automated computation of the accuracy parameter [Formula: see text], thereby eliminating the need for manual selection. This results in improved performance with simplified parameter tuning. The effectiveness of the approach in this paper be demonstrated through a numerical example.