Title :
SVM for Solving Forward Problems of EIT
Author :
Wu, Youxi ; Li, Ying ; Guo, Lei ; Yan, Weili ; Shen, Xueqin ; Fu, Kun
Author_Institution :
Sch. of Comput. Sci. & Software, Hebei Univ. of Technol., Tianjin
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
Keywords :
electric impedance imaging; inverse problems; learning (artificial intelligence); medical image processing; partial differential equations; regression analysis; support vector machines; EIT; SVM; forward problems; image classification; inverse problems; machine learning; partial differential equations; structural risk minimization; support vector machine; support vector regression; Conductivity; Differential equations; Inverse problems; Laplace equations; Partial differential equations; Risk management; Support vector machine classification; Support vector machines; Virtual colonoscopy; Voltage;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1616732
