Published Paper Details:

Machine Learning is one of the major popular research topics of Artificial Intelligence and it pass on with the evolution of techniques and methods which enable the data processor to learn and execute activities. 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 Statistical Learning Theory cannot procure location information in a Sentient 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 hyperplane 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 highdimensional feature space by a nonlinearly transformation for the given samples. SVM gives a very low error rate when used for classification. Here proposed a new SVM based approach for Vehicle Positioning and Tracking in GSM network.