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The insensitive parameter in support vector regression determines the set ofsupport vectors that greatly impacts the prediction. A data-driven approach isproposed to determine an approximate value for this insensitive parameter byminimizing a generalized loss function originating from the likelihoodprinciple. This data-driven support vector regression also statisticallystandardizes samples using the scale of noises. Nonlinear and linear numericalsimulations with three types of noises ($\epsilon$-Laplacian distribution,normal distribution, and uniform distribution), and in addition, five realbenchmark 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 supportvector regression using a working likelihood, data-driven insensitive parameteris superior and has lower computational costs.

A working likelihood approach to support vector regression with a data-driven insensitivity parameter