A Novel Approach for Data Classification Using Neural Network

In this paper, an approach of multi-view learning, with multilayer perceptron (MLP) and radial basis functions (RBF) with dynamic decay adjustment (DDA), has been proposed. Three different categories of semi-supervised learning are multi-view training, co-training and self-training. Here we have only used self-training and multi-view learning mechanisms to train the classifier. To test the accuracy of the algorithms, we have taken five real-time datasets from UCI Machine Learning Repository. The classifier is trained using the perceptron learning rule with its supervised and semi-supervised (self-training) versions and MLP with RBF (multi-view learning). The average classification accuracies have been compared and the proposed algorithm outperforms the former versions on the specified training sets. The significant improvement in performance obtained using multi-view learning can be used for various fields such as detecting changes of images, speech recognition and biometric identification.