论文标题
使用数据驱动的不敏感参数支持向量回归的一种工作可能性方法
A working likelihood approach to support vector regression with a data-driven insensitivity parameter
论文作者
论文摘要
支持向量回归中的不敏感参数确定了极大地影响预测的支持向量的集合。提出了一种数据驱动的方法来通过最大程度地减少源自可能性原理的广义损耗函数来确定此不敏感参数的近似值。该数据驱动的支持向量回归还使用噪声规模在统计上标准化样本。具有三种类型的噪声（$ε$ -LAPLACIAN分布，正态分布和均匀分布）的非线性和线性数值模拟，此外，还使用了五个实际基准数据集来测试所提出方法的容量。基于所有模拟和五个案例研究，提出的支持向量回归使用工作可能性，数据驱动的不敏感参数较高，并且具有较低的计算成本。
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.
