Date of Original Version



Conference Proceeding

Abstract or Description

Many application domains suffer from not having enough labeled training data for learning. However, large amounts of unlabeled examples can often be gathered cheaply. As a result, there has been a great deal of work in recent years on how unlabeled data can be used to aid classification. We consider an algorithm based on finding minimum cuts in graphs, that uses pairwise relationships among the examples in order to learn from both labeled and unlabeled data.



Published In

Proceedings of the Eighteenth international Conference on Machine Learning. C. E. Brodley and A. P. Danyluk, Eds. , 19-26.