计算机与现代化Issue(8)：1-4,4.DOI:10.3969/j.issn.1006-2475.2017.08.001
一种启发式线性回归损失函数选取方法
A Heuristic Linear Regression Loss Function Selection Method
张祎1
作者信息
- 1. 雅安职业技术学院机电与信息工程系,四川 雅安 625000
- 折叠
摘要
Abstract
Loss function is used to quantify information loss and false degree in regression analysis.This paper addresses heuristic loss function selection for linear regression.For a given noise density, there exists an optimal loss function under an asymptotic setting i.e.squared loss is optimal for Gaussian noise density.However, in real-life applications the noise density is always unknown and the training samples are finite.Robust statistics provides ways for selecting the loss function using statistical information about noise density, however robust statistics is based on asymptotic assumption and may not be well applied for finite sample data sets.For such practical problems, we try to utilize concept of Vapnik''s ε-insensitive loss function.We propose a heuristic method for setting the value of ε as a function of samples and noise variance.Experimental comparisons for linear regression problems show that the proposed loss function performs more robustly performance and yields higher prediction accuracy compared with popular squared loss and Huber''s least-modulus loss.关键词
损失函数/支持向量机/平方损失函数/参数选择/VC维Key words
loss function/support vector machine/square loss function/parameter selection/VC dimension分类
天文与地球科学引用本文复制引用
张祎..一种启发式线性回归损失函数选取方法[J].计算机与现代化,2017,(8):1-4,4.
