Image Reconstruction Using Multi Layer Perceptron (MLP) And Support Vector Machine (SVM) Classifier And Study Of Classification Accuracy
Shovasis Kumar Biswas, Mohammad Mahmudul Alam Mia
Index Terms: Neural networks, Classification, Support Vector Machine, Kernel functions, Multi layer perceptron.
Abstract: Support Vector Machine (SVM) and back-propagation neural network (BPNN) has been applied successfully in many areas, for example, rule extraction, classification and evaluation. In this paper, we studied the back-propagation algorithm for training the multilayer artificial neural network and a support vector machine for data classification and image reconstruction aspects. A model focused on SVM with Gaussian RBF kernel is utilized here for data classification. Back propagation neural network is viewed as one of the most straightforward and is most general methods used for supervised training of multilayered neural network. We compared a support vector machine (SVM) with a back-propagation neural network (BPNN) for the task of data classification and image reconstruction. We made a comparison between the performances of the multi-class classification of these two learning methods. Comparing with these two methods, we can conclude that the classification accuracy of the support vector machine is better, and algorithm is much faster than the MLP with back propagation algorithm.
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