Semi-supervised induction of fuzzy rules applied to image segmentation
Aljoscha Klose, J. Schneider · 2002
In many applications huge amounts of data are available. However, these are often unlabeled and the user must manually assign labels. The idea of semi-supervised learning is to use as much labeled data as available and try to additionally exploit the structure in the unlabeled data. In this paper we describe an approach to semi-supervised learning of fuzzy systems. Our work is targeted at supporting object tracking in images.