Spatially adaptive sparse grids for high-dimensional data-driven problems
Journal of Complexity
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
A comparative study on machine learning techniques for prediction of success of dental implants
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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The Dynamic Decay Adjustment (DDA) algorithm is a fast constructive algorithm for training RBF neural networks. In previous works it has been shown that for some datasets the generalization performance of RBF-DDA depends only weakly on the algorithm parameters 驴+ and 驴-. However, we have observed experimentally that for some problems performance is considerably dependent on the value of 驴-. In this work we propose a method for selecting the value of 驴- for performance optimization. The proposed method has been evaluated on three optical recognition datasets from the UCI repository. The results show that the proposed method considerably improves the performance of RBF-DDA with default parameters on these tasks. The results are compared to MLP and k-NN results obtained in previous works. It is shown that the method proposed in this paper outperforms MLPs and obtains results comparable to k-NN on these tasks.