Vehicle tracking and positioning in GSM network using optimized SVM model

Vehicle Tracking and positioning in GSM networkwith greater accuracy is one of the major popular research topics of Intelligence transportation System and it pass on with the evolution of techniques and methods which enable the data processor to learn and execute activities with the help of Machine learning. Support Vector Machine (SVM) is an isolated classifier which deals with both linear and nonlinear data from hyper-plane with the help of Supervised Learning Approach. Whereas the Statistical Learning Theory was unable to procure location information in a Mobile Computing because of functional dependencies of geographic coordinates from RSSI but SVM can predict the location fingerprint with regression estimation and linear operator inversion and realize the actual risk minimization by structural risk minimization. SVM can also deliver a good learning outcome in the face of less sample volume. The basic idea of SVM is for linearly separable samples, to find the optimal classification hyper-plane which can be described accurately and the samples are separated into two categories for the linearly non-separable problems; to transform the linear non-separable problems in the original space into the linearly separable problems in high-dimensional feature space by a nonlinearly transformation for the given samples of dataset. SVM gives a very low error rate when used for classification.