Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
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The process of mixing labelled and unlabelled data is being recently studied in semi-supervision techniques. However, this is not the only scenario in which mixture of labelled and unlabelled data can be done. In this paper we propose a new problem we have called particularization and a way to solve it. We also propose a new technique for mixing labelled and unlabelled data. This technique relies in the combination of supervised and unsupervised processes competing for the classification of each data point. Encouraging results on improving the classification outcome are obtained on MNIST database.