Speed-up of the R4-rule for distance-based neural network learning

The R⁴-rule is a heuristic algorithm for distance-based neural network (DBNN) learning. Experimental results show that the R⁴-rule can obtain the smallest or nearly smallest DBNNs. However, the computational cost of the R⁴-rule is relatively high because the learning vector quantization (LVQ) algorithm is used iteratively during learning. To reduce the cost of the R⁴-rule, we investigate three approaches in this paper. The first one is called the distance preservation (DP) approach, which tries to reduce the number of times for calculating the distance values, and the other two are based on the attentional learning concept, which try to reduce the number of data used for learning. The efficiency of these methods is verified through experiments on several public databases.