Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Self-Organizing Maps
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Fuzzy-input fuzzy-output one-against-all support vector machines
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Fuzzy Gaussian Process Classification Model
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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Prototype based classifiers so far can only work with hard labels on the training data. In order to allow for soft labels as input label and answer, we enhanced the original LVQ algorithm. The key idea is adapting the prototypes depending on the similarity of their fuzzy labels to the ones of training samples. In experiments, the performance of the fuzzy LVQ was compared against the original approach. Of special interest was the behaviour of the two approaches, once noise was added to the training labels, and here a clear advantage of fuzzy versus hard training labels could be shown.