Abstract
This work describes a learning vector quantization (LVQ) method for unsupervised neural networks for classification tasks. We work with a modular architecture of this method, so we can classify three classes per module. We also work with three different databases, the arrhythmia database from MIT-BIH, which contains 15 different classes, a character database from UCI with 26 different classes, and finally a vehicle silhouettes database also from UCI with 4 different classes.
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Amezcua, J., Melin, P., Castillo, O. (2016). A Neural Network with a Learning Vector Quantization Algorithm for Multiclass Classification Using a Modular Approach. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-32229-2_13
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DOI: https://doi.org/10.1007/978-3-319-32229-2_13
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