FlexNet-A flexible neural network construction algorithm

摘要

Dynamic neural network algorithms are used automatic network design in order to avoid time consuming search for finding an appropriate network topology with trial & error methods. The new FlexNet algorithm, unlike other network construction algorithms, does not underlie any constraints regarding the number of hidden layers and hidden units. In addition different connection strategies are avilable, together with candidate pool training and the option of freezing weights. Test results on 3 different benchmarks showed higher generalization rates for FlexNet compared to Cascade-Correlation and optimized static MLP networks.