Improving RBF-DDA Performance on Optical Character Recognition through Parameter Selection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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Dynamic decay adjustment (DDA) is a fast algorithm to construct radial basis function (RBF) networks for classification problems. It is known that, despite its interesting features, DDA produces classifiers with high complexity, especially for large datasets. In this Letter a simple method to overcome this problem is proposed, which eliminates redundant units improving generalization. Experimental results on benchmark datasets show the good performance of our approach compared to previous methods.