Fast structural learning of distance-based neural networks

The R⁴-rule is a structural learning algorithm for obtaining the smallest or nearly smallest distance-based neural networks. However, the computational cost of the R⁴-rule is relatively high because the learning vector quantization (LVQ) algorithm is used iteratively. To reduce the cost of the R⁴-rule, we investigate two approaches in this paper. The first one is called attentional learning (AL) which tries to reduce the number of data used for learning. The second one is called distance preservation (DP), which tries to reduce the number of times for calculating the distances during learning. The efficiency of these two approaches as well as their combination is verified through experiments on several public databases.