Bayesian text classifiers face a common issue which is referred to as data sparsity problem, especially when the size of training data is very small. The frequently used Laplacian...
We study a class of functional which can be used for matching objects which can be represented as mappings from a fixed interval, I, to some "feature space." This class o...
This paper presents a novel solution for the problem of building text classifier using positive documents (P) and unlabeled documents (U). Here, the unlabeled documents are mixed w...
Information available in the Internet is frequently supplied simply as plain ascii text, structured according to orthographic and semantic conventions. Traditional document classi...