The Effect of Bottlenecks on Generalisation in Backpropagation Neural Networks
Many modifications have been proposed to improve back-propagation'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...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the International Conference on Neural Information Processing (ICONIP 2010)|
|01_Zang_The_Effect_of_Bottlenecks_on_2010.pdf||370.53 kB||Adobe PDF||Request a copy|
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