Abstract
This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ. FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functions, and kernel-induced distance measures. We compare FKLVQ with the well-known fuzzy LVQ and the recently proposed fuzzy-soft LVQ on some artificial and real data sets. Experimental results show that FKLVQ is more accurate and needs far fewer iteration steps than the latter two algorithms. Moreover FKLVQ shows good robustness to outliers.
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Zhang, D., Chen, S., Zhou, ZH. (2004). Fuzzy-Kernel Learning Vector Quantization. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_31
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DOI: https://doi.org/10.1007/978-3-540-28647-9_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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