REGRESSION MODELS USING PATTERN SEARCH ASSISTED LEAST SQUARE SUPPORT VECTOR MACHINES

摘要

Least Square Support Vector Machines (LS-SVM),a new machine-learning tool has been employed for developing data driven models of non-linear processes.The method is firmly rooted in the statistical learning theory and transforms the input data to a higher dimensional feature space where the use of appropriate kernel functions avoid computational difficulty.Further,a pattern search algorithm,which explores multiple directions and utilizes coordinate search with fixed step size,is employed for selecting optimal LS-SVM model that produces a minimum possible prediction error.To show the efficacy and efficiency of the fully automated pattern search assisted LS-SVM methodology,we have tested it on several benchmark examples.The study suggests that proposed paradigm can be a useful and viable tool in building data driven models of non-linear processes.