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Support vector machine (SVM) is powerful to solve problems such as nonlinear classification, function estimation and density estimation. It has also led to many other recent developments in kernel-based learning fields. In this paper, we extend a high-accuracy, real-time, and fault-tolerant SVM to mobile geo-location problem, which has become an important component of pervasive computing. Simulation results show its basic location performance superior to conventional least square (LS) algorithm especially under nonlight of sight (NLOS) environments. Finally, we also analyze the impacts of training samples and training area on test location accuracy.