Creating artificial neural networks that generalize
Neural Networks
Heuristic pattern reduction II
ICYCS'93 Proceedings of the third international conference on Young computer scientists
Balancing Bias and Variance: Network Topology and Pattern Set Reduction Techniques
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
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Many modifications have been proposed to improve backpropagation's convergence time and generalisation capabilities. Typical techniques involve pruning of hidden neurons, adding noise to hidden neurons which do not learn, and reducing dataset size. In this paper, we wanted to compare these modifications' performance in many situations, perhaps for which they were not designed. Seven famous UCI datasets were used. These datasets are different in dimension, size and number of outliers. After experiments, we find some modifications have excellent effect of decreasing network's convergence time and improving generalisation capability while some modifications perform much the same as unmodified back-propagation. We also seek to find a combine of modifications which outperforms any single selected modification.