电子科技大学学报Issue(1)：131-136,6.DOI:10.3969/j.issn.1001-0548.2014.01.022
截断误差的光滑型支持向量顺序回归
Truncated Loss Smooth Support Vector Ordinal Regression
摘要
Abstract
Support vector ordinal regression (SVOR) has been proven to be the promising algorithm for solving ordinal regression problems. However, its performance tends to be strongly affected by outliers in the training datasets. To remedy this drawback, a truncated loss smooth SVOR (TLS-SVOR) is proposed. While learning ordinal regression models, the loss s of the misranked sample is bounded between 0 and the truncated coefficient u. First, a piecewise polynomial function with parameter u is approximated to s. Then, by applying the strategy of smooth support vector machine for classification, the optimization problem is replaced with an unconstrained function which is twice continuously differentiable. The algorithm employs Newton’s method to obtain the unique discriminant hyperplane. The optimal parameter combination of TLS-SVOR is determined by a two-stage uniform designed model selection methodology. The experimental results on benchmark datasets show that TLS-SVOR has advantage in terms of accuracy over other ordinal regression approaches.关键词
顺序回归/野点/分段多项式/支持向量机/截断误差Key words
ordinal regression/outlier/piecewise polynomial/support vector machine/truncated loss分类
信息技术与安全科学引用本文复制引用
何海江..截断误差的光滑型支持向量顺序回归[J].电子科技大学学报,2014,(1):131-136,6.基金项目
国家自然科学基金(61100139) （61100139）
