Details Research Areas Interests:

 In recent years, a novel machine learning technique called Support Vector Machine (SVM), proposed by Vapnik based on statistical learning theory has been successfully applied to pattern classification and regression estimation problems. Initially SVMs were developed for solving classification problems and later they have been extended by Vapnik et al.to regression problems. Unlike Artificial Neural Networks (ANNs) which are based on Empirical Risk Minimization (ERM) induction princi ple, SVM implements Structural Risk Minimization (SRM) principle. It is well known that ANN has the problem of overfitting and that the solution may get trapped into local minima’s whereas SRM principle aims in minimizing an upper bound on the generalization error and thus SVM will have higher prediction capabilities on unseen dataset. Further, SVM formulation will lead to the solution of a quadratic optimization problem with linear inequality constraints and that the problem will have a global optimal solution. Combined with high generalization ability SVM becomes a very attractive method. A very successful approach for regression problems is Support Vector Regression (SVR) method studied by Vapnik and is an extension of SVM initially proposed for classification problems. Further, Vapnik introduced -insensitive error loss function. SVMs have been successfully applied to a large number of class of problems of importance like character recognition, text characterization, face detection, time series chaoticity, image processing, watermarking, information retrieval, gene sequence analysis, micro-array data and gene expression, ECG and EEG time series analysis, brain time series analysis (especially abnormal brain activity due to Epilepsy), drug discovery, financial time series forecasting, credit scoring, stock exchange prediction, blind identification, nonlinear equalization in mobile communication system and analysis of dynamical system time series like Lorenz series, Mackey Glass series, Hennon time series etc .

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