Abstract
In this book the Fuzz LVQ method for classification tasks is presented. This new method is based on the hybridization of artificial neural networks with the LVQ algorithm and type-2 fuzzy logic. Classification of information can be a complicated task. In general terms, for working with LVQ networks, and some other classification methods, it is important to thoroughly analyze the information and determine the most representative attributes. This is helpful by itself and avoids an overload of information for the method.
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Reference
Rubio, E., & Castillo, O. (2013). Interval type-2 fuzzy clustering for membership function generation (pp. 13–18). Singapore: 2013 IEEE Workshop on Hybrid Intelligent Models and Applications (HIMA).
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Amezcua, J., Melin, P., Castillo, O. (2018). Conclusions. In: New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-73773-7_6
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DOI: https://doi.org/10.1007/978-3-319-73773-7_6
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73772-0
Online ISBN: 978-3-319-73773-7
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