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

支持向量回归的几何方法及其在发酵过程快速建模中的应用(英文)

作　　者：王建林 冯絮影 于涛 

机构地区：[1]College of Information Science and Technology,Beijing University of Chemical Technology

出　　处：《Chinese Journal of Chemical Engineering》2012年第4期715-722,共8页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (20476007,20676013)

摘　　要：Support vector machine(SVM) has shown great potential in pattern recognition and regressive estima-tion.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 re-gression(SVR) is derived,and μ-SVM is extended for both L1-norm and L2-norm penalty SVR.Further,Gilbert al-gorithm,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 computa-tion as their corresponding algorithms for SVM classification.Experimental results show that the geometric meth-ods are more efficient than conventional methods using quadratic programming and require much less memory.Support vector machine（SVM） has shown great potential in pattern recognition and regressive estima-tion.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 re-gression（SVR） is derived,and μ-SVM is extended for both L1-norm and L2-norm penalty SVR.Further,Gilbert al-gorithm,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 computa-tion as their corresponding algorithms for SVM classification.Experimental results show that the geometric meth-ods are more efficient than conventional methods using quadratic programming and require much less memory.

关 键 词：support vector machine pattern recognition regressive estimation geometric algorithms 

分 类 号：TQ920.6[轻工技术与工程—发酵工程]