Decremental learning based on sample-weighted Support Vector Regression

Author

Li Qing ; Wang Ling ; Zhang De Zheng ; Zhang Wei Cun

Author_Institution

Sch. of Autom. & Electr. Eng., Univ. of Sci. & Technol., Beijing, China

fYear

2012

fDate

23-25 May 2012

Firstpage

1322

Lastpage

1325

Abstract

In this paper, a new modeling method-decremental learning based on sample-weighted SVR(DSWSVR) is proposed, which introduces the decremental learning strategy into sample selection based on support vector regression (SVR). DSWSVR differs from SVR in that it builds a new sample set, where some sample in the original sample set are weighted differently to account for its representative to improve the prediction ability of the algorithm. Simulation results show that the proposed algorithm can improve the performance of the SVR modeling.

Keywords

genetic algorithms; learning (artificial intelligence); regression analysis; support vector machines; DSWSVR; SVR modeling; SVR-based sample selection; decremental learning strategy; modeling method-decremental; prediction ability; sample set; sample-weighted support vector regression; support vector regression-based sample selection; Computational modeling; Genetic algorithms; Prediction algorithms; Predictive models; Steel; Support vector machines; Training; Decremental Learning; Genetic Algorithm; Support Vector Regression (SVR); Weighted Sample;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2012 24th Chinese

Conference_Location

Taiyuan

Print_ISBN

978-1-4577-2073-4

Type

conf

DOI

10.1109/CCDC.2012.6244212

Filename

6244212