Classification learning by prototype based approaches is an attractive strategy to achieve interpretable classification models. Frequently, those models optimize the classification error or an approximation thereof. Current deep network approaches use the cross entropy maximization instead. Therefore, we propose a prototype based classifier based on cross-entropy as a probabilistic classifier. As we deduce, the proposed probabilistic classifier is a generalization of the robust soft-learning vector quantizer and allows to handle label noise in training data, i.e. the classifier is able to take into account probabilistic class assignments during learning.
CITATION STYLE
Villmann, A., Kaden, M., Saralajew, S., & Villmann, T. (2018). Probabilistic learning vector quantization with cross-entropy for probabilistic class assignments in classification learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10841 LNAI, pp. 724–735). Springer Verlag. https://doi.org/10.1007/978-3-319-91253-0_67
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