Many application domains such as web page classification suffer from not having enough labeled training examples for learning. However, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. As a result, there has been a great deal of work in resent years on semi-supervised learning. This paper proposes a graph-based semi-supervised learning algorithm that is applied to the web page classification. Our algorithm uses a similarity measure between web pages to construct a K-Nearest Neighbor graph. Labeled and unlabeled web pages are represented as node...
