A Geometric Approach to Support Vector Regression and Its Application to Fermentation Process Fast Modeling

王建林; 冯絮影; 于涛

doi:10.1016/S1004-9541(11)60240-1

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A Geometric Approach to Support Vector Regression and Its Application to Fermentation Process Fast Modeling

Updated：2026-01-06

- A Geometric Approach to Support Vector Regression and Its Application to Fermentation Process Fast Modeling
- A Geometric Approach to Support Vector Regression and Its Application to Fermentation Process Fast Modeling
- 中国化学工程学报（英文版） 2012年20卷第4期页码：715-722
- Affiliations：
  
  College of Information Science and Technology, Beijing University of Chemical Technology,Beijing,China,100029
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(11)60240-1
  中图分类号：
- 纸质出版：2012
- Accepted：
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王建林, 冯絮影, 于涛. A Geometric Approach to Support Vector Regression and Its Application to Fermentation Process Fast Modeling[J]. 中国化学工程学报（英文版）, 2012,20(4):715-722.

WANG Jianlin, FENG Xuying, YU Tao. A Geometric Approach to Support Vector Regression and Its Application to Fermentation Process Fast Modeling[J]. Chin.J.Chem.Eng., 2012, 20(4): 715-722.
王建林, 冯絮影, 于涛. A Geometric Approach to Support Vector Regression and Its Application to Fermentation Process Fast Modeling[J]. 中国化学工程学报（英文版）, 2012,20(4):715-722. DOI： 10.1016/S1004-9541(11)60240-1.

WANG Jianlin, FENG Xuying, YU Tao. A Geometric Approach to Support Vector Regression and Its Application to Fermentation Process Fast Modeling[J]. Chin.J.Chem.Eng., 2012, 20(4): 715-722. DOI： 10.1016/S1004-9541(11)60240-1.

摘要

Abstract

Support vector machine(SVM) has shown great potential in pattern recognition and regressive estimation.Due to the industrial development demands

such as the fermentation process modeling

improving the training performance on increasingly large sample sets is an important problem.However

solving a large optimization problem is computationally intensive and memory intensive.In this paper

a geometric interpretation of SVM regression(SVR) is derived

and

-SVM is extended for both

-norm and

-norm penalty SVR.Further

Gilbert algorithm

a well-known geometric algorithm

is modified to solve SVR problems.Theoretical analysis indicates that the presented SVR training geometric algorithms have the same convergence and almost identical cost of computation as their corresponding algorithms for SVM classification.Experimental results show that the geometric methods are more efficient than conventional methods using quadratic programming and require much less memory.

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