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Research Paper | Computer Science & Engineering | India | Volume 2 Issue 11, November 2013 | Rating: 6.7 / 10
SVM: The Qualitative and Quantitative Monolithic Predictor
Abhijit Adhikari, Chetan Nimba Aher
Abstract: Machine Learning is one of the major popular research topics of Artificial Intelligence and its relay with the evolution of techniques and methods which enable the data processor to learn and execute activities. Support Vector Machine (SVM) is a segregated classifier deal with both linear and nonlinear data from hyperplane 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, so that SVM can also obtain a good learning outcome in the face of less sample volume. The basic idea of SVM is for the linearly separable samples, to find the optimal classification hyperplane which can describe accurately the samples into two categories for the linearly nonseparable 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. SVM gives a very low error rate when used as a classifier.
Keywords: Support Vector Machine SVM, Received Signal Strength Identity RSSI, Hyperplane, Location Fingerprint, Statistical Learning
Edition: Volume 2 Issue 11, November 2013,
Pages: 270 - 273