Support vector regression model with variant tolerance

Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius [Formula: see text]-tube, affording good predictive performance on datasets. However, the fixed radius limitation prevents the adaptive selection of support vectors according to the data distribution characteristics, compromising the performance of the SVR-based methods. Therefore, this study proposes an “Alterable [Formula: see text]-Support Vector Regression” ([Formula: see text]-SVR) model by applying a novel [Formula: see text], named “Alterable [Formula: see text],” to the SVR model. Based on the data point sparsity at each location, the model solves the different [Formula: see text] at the corresponding position, and thus zoom-in or zoom-out the [Formula: see text]-tube by changing its radius. Such a variable [Formula: see text]-tube strategy diminishes noise and outliers in the dataset, enhancing the prediction performance of the [Formula: see text]-SVR model. Therefore, we suggest a novel non-deterministic algorithm to iteratively solve the complex problem of optimizing [Formula: see text] associated with every location. Extensive experimental results demonstrate that our approach can improve the accuracy and stability on simulated and real data compared with the baseline methods.