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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Probabilistic Prototype Classification Using t-norms
verfasst von : Tina Geweniger, Frank-Michael Schleif, Thomas Villmann
Erschienen in: Advances in Self-Organizing Maps and Learning Vector Quantization
Verlag: Springer International Publishing
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We introduce a generalization of Multivariate Robust Soft Learning Vector Quantization. The approach is a probabilistic classifier and can deal with vectorial class labelings for the training data and the prototypes. It employs t-norms, known from fuzzy learning and fuzzy set theory, in the class label assignments, leading to a more flexible model with respect to domain requirements. We present experiments to demonstrate the extended algorithm in practice.