Learning with Labeled and Unlabeled Data

Semi-supervised learning belongs to the main directions of the recent machine learning research. The exploitation of the unlabeled data is an attractive approach either to extend the capability of the known methods or to derive novel learning devices.  Learning a rule from a finite sample is the fundamental problem of machine learning. For this purpose two resources are needed: a big enough sample and enough computational power. While the computational power has been growing rapidly, the cost of collecting a large sample remains high since it is labour intensive. The unlabeled data can be used to find a compact representation of the data which preserves as much as possible its original structure.