Boosting a weak learning algorithm by majority
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Neural networks and the bias/variance dilemma
Neural Computation
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Self-Organizing Maps
Machine Learning
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Ensemble learning in linearly combined classifiers via negative correlation
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
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In many practical cases, only few labels are available on the data. Algorithms must then take advantage of the unlabeled data to ensure an efficient learning. This type of learning is called semi-supervised learning (SSL). In this article, we propose a methodology adapted to both the representation and the prediction of large datasets in that situation. For that purpose, groups of non-correlated attributes are created in order to overcome problems related to high dimensional spaces. An ensemble is then set up to learn each group with a self-organizing map (SOM). Beside the prediction, these maps also aim at providing a relevant representation of the data which could be used in semi-supervised learning. Finally, the prediction is achieved by a vote of the different maps. Experimentations are performed both in supervised and semi-supervised learning. They show the relevance of this approach.