Improving RBF-DDA Performance on Optical Character Recognition through Weights Adjustment

The dynamic decay adjustment (DDA) algorithm is a fast constructive algorithm for training RBF neural networks. This paper proposes a method for improving RBF-DDA generalization performance by adjusting the weights of the connections between hidden and output units. The method proposed here has been evaluated on three optical character recognition datasets from the UCI repository. The results show that the proposed method considerably improves performance of RBF-DDA in these tasks without increasing the size of the networks. The results are compared to MLP, k-NN, AdaBoost and SVM results reported in the literature. It is shown that the proposed method outperforms MLP and AdaBoost and obtains results comparable to k-NN and SVM on these datasets.