Speed-up of the R4-rule for Distance-Based Neural Network Learning
نویسندگان
چکیده
The R-rule is a heuristic algorithm for distancebased neural network (DBNN) learning. Experimental results show that the R-rule can obtain the smallest or nearly smallest DBNNs. However, the computational cost of the R-rule is relatively high because the learning vector quantization (LVQ) algorithm is used iteratively during learning. To reduce the cost of the R-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.
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