Linear and support vector regressions based on geometrical correlation of data

Data Science Journal

Lixin Guo¹  Chongyang Tu²  Junying Zhang²  Kaijun Wang² 

[1] Dept of Computer Science, Xian Institute of Post-telecommunications;School of Computer Science and Engineering, Xidian University

关键词: Geometrical correlation learning;    Geometrical correlation of data;    Regression analysis;   

DOI  :  10.2481/dsj.6.99

学科分类：计算机科学（综合）

来源: Ubiquity Press Ltd.

【 摘 要 】

References(12)Linear regression (LR) and support vector regression (SVR) are widely used in data analysis. Geometrical correlation learning (GcLearn) was proposed recently to improve the predictive ability of LR and SVR through mining and using correlations between data of a variable (inner correlation). This paper theoretically analyzes prediction performance of the GcLearn method and proves that GcLearn LR and SVR will have better prediction performance than traditional LR and SVR for prediction tasks when good inner correlations are obtained and predictions by traditional LR and SVR are far away from their neighbor training data under inner correlation. This gives the applicable condition of GcLearn method.

【 授权许可】

Unknown