Soft learning vector quantization
Neural Computation
Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Experiments with Supervised Fuzzy LVQ
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
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
A study of the robustness of KNN classifiers trained using soft labels
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
IEEE Transactions on Neural Networks
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Soft labels allow a pattern to belong to multiple classes with different degrees. In many real world applications the association of a pattern to multiple classes is more realistic; to describe overlap and uncertainties in class belongingness. The objective of this work is to develop a fuzzy Gaussian process model for classification of soft labeled data. Gaussian process models have gained popularity in the recent years in classification and regression problems and are example of a flexible, probabilistic, non-parametric model with uncertainty predictions. Here we derive a fuzzy Gaussian model for a two class problem and then explain how this can be extended to multiple classes. The derived model is tested on different fuzzified datasets to show that it can adopt to various classification problems. Results reveal that our model outperforms the fuzzy K-Nearest Neighbor (FKNN), applied on the fuzzified dataset, as well as the Gaussian process and the K-Nearest Neighbor models used with crisp labels.