A HYBRID LINEAR KERNEL WITH PCA IN SVM PREDICTION MODEL OF TAMIL WRITING PATTERN

Abstract

Principal Component Regression (PCR) is a regression analysis technique based on Principal Component Analysis (PCA) which enables the identification of the principal components that can be used in a linear kernel and Support Vector Machine (SVM) as a classifier. In PCR, instead of regressing the dependent variable on the explanatory variables directly, the principal components of the explanatory variables are used as regresseors. Only a subset of all the principal components is made use of for regression, thus making PCR a kind of regularized procedure. The principal components with higher variances are selected as regressors and used in SVM linear kernel to estimate the coefficients of the kernel and the linear kernel is specified as Principal Component Kernel–Support Vector Machine (PCK-SVM). Writer Identification in Tamil handwriting is implemented by employing PCK-SVM and the results of the PCK-SVM are compared with our Weighted Least Square regression Kernel based Support Vector Machine (WLK-SVM) and Bayesian linear regression Kernel based Support Vector Machine (BLK-SVM)models. These methods are evaluated on several text images of handwriting at character, word and paragraph levels. The results show that modified linear kernel performs very well with minimum time taken to classify the writer. Performance comparison results of three kernels achieved highest performance of 94.9% accuracy in PCK-SVM than in WLK of 90.8% and BLK of 92.3% accuracy.