Exploring the factor effect of learning vector quantization in artificial neural networks

Abstract

The goal of this study is to explore the factor effect of learning vector quantization. The manipulated factors are training pattern, learning rate, types of mixed data, and hidden node. The results showed that the average accuracy for severe overlap data was significantly lower than for those of slight and moderate overlap data. The worst classification accuracy was found for mixed data with learning rate equals to 0.1; whereas the best classification accuracy was found when the number of hidden nodes and output categories are equal. As a result, the classification accuracy increased as the number of training patterns increased. Conclusions and discussions are provided for practical guidelines.