2020/03/09 by Jinran Wu, You-Gan Wang, You‐Gan Wang +2 · 1 citation
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Computation (stat.CO) #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Inference #cs.LG #stat.CO #stat.ML
paper · pdf · doi:10.48550/arxiv.2003.03893
arxiv created 2020/03/09 · openalex publication_date 2020/03/09 · arxiv updated 2020/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The insensitive parameter in support vector regression determines the set of support vectors that greatly impacts the prediction. A data-driven approach is proposed to determine an approximate value for this insensitive parameter by minimizing a generalized loss function originating from the likelihood principle. This data-driven support vector regression also statistically standardizes samples using the scale of noises. Nonlinear and linear numerical simulations with three types of noises (ε-Laplacian distribution, normal distribution, and uniform distribution), and in addition, five real benchmark data sets, are used to test the capacity of the proposed method. Based on all of the simulations and the five case studies, the proposed support vector regression using a working likelihood, data-driven insensitive parameter is superior and has lower computational costs.