Abstract
Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel based methods in general. This paper presents a high-accuracy and fault-tolerant SVM for the mobile geo-location problem, which is an important component of pervasive computing. Simulation results show its basic location performance, and illustrate impacts of the number of training samples and training area on test location error.
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Communication author: Sun Guolin, born in 1978, male, Ph.D. candidate. National Key Lab. of Communication, UESTC, Chengdu 610054, China.
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Sun, G., Guo, W. Mobile geo-location algorithm based on LS-SVM. J. of Electron.(China) 22, 351–356 (2005). https://doi.org/10.1007/BF02687921
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DOI: https://doi.org/10.1007/BF02687921