摘要
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 highaccuracy 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.
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.
