Abstract
Due to their rather intuitive and understandable application fuzzy if-then rules are a popular basis for classifiers. In many practical applications huge amounts of data are available for analysis. However, these are often unlabeled and the user must manually assign labels. The idea of semi-supervised learning is to use as much labeled data as available and try to additionally exploit the structure in the unlabeled data. In this paper we describe an approach to enable semi-supervised learning for (neuro-) fuzzy systems.
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Klose, A., Girimonte, D., Kruse, R. (2003). Extending Neuro-Fuzzy Classification to Semi-Supervised Learning. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_80
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DOI: https://doi.org/10.1007/978-3-7908-1902-1_80
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0005-0
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