title: SVM for Solving Forward Problems of EIT.
abstract: Support Vector Machine (SVM) can be seen as a new machine learning way which is based on the idea of VC dimensions and the principle of structural risk minimization rather than empirical risk minimization. SVM can be used for classification and regression. Support Vector Regression (SVR) is a very important branch of Support Vector Machine. Partial Differential Equations (PDEs) have been successfully treated by using SVR in previous works. The forward problems of EIT are the basis of EIT inverse problems. The forward problem's essence is to solve PDEs. The method has been successfully tested on the forward problems of EIT and has yielded accurate results.
authors.affiliation: School of Computer Science and Software, Hebei University of Technology, Tianjin, China. (phone: +86-22-60204133; e-mail: wuc567@163.com).
authors.forename: Youxi
authors.initials: Y
authors.lastname: Wu
authors.forename: Ying
authors.initials: Y
authors.lastname: Li
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Weili
authors.initials: W
authors.lastname: Yan
authors.forename: Xueqin
authors.initials: X
authors.lastname: Shen
authors.forename: Kun
authors.initials: K
authors.lastname: Fu