Universum Learning for SVM Regression

被引：0

作者：

Dhar, Sauptik ^{[1
]}

Cherkassky, Vladimir ^{[2
]}

机构：

[1] Robert Bosch LLC, Res & Technol Ctr, Palo Alto, CA 94304 USA

[2] Univ Minnesota, Dept Elect & Comp Engn, Minneapolis, MN 55455 USA

来源：

2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2017年

关键词：

Support Vector Regression; learning through contradiction; Universum Learning;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper extends the idea of Universum learning to regression problems. We propose new Universum-SVM formulation for regression problems that incorporates a priori knowledge in the form of additional data samples. These additional data samples, or Universum samples, belong to the same application domain as the training samples, but they follow a different distribution. Several empirical comparisons are presented to illustrate the utility of the proposed approach.

引用

页码：3641 / 3648

页数：8

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