Traditional text classification algorithms are based on a basic assumption: the training and test data should hold the same distribution. However, this identical distribution assum...
Abstract. Ontology classification--the computation of subsumption hierarchies for classes and properties--is one of the most important tasks for OWL reasoners. Based on the algorit...
Birte Glimm, Ian Horrocks, Boris Motik, Giorgos St...
Consider a supervised learning problem in which examples contain both numerical- and text-valued features. To use traditional featurevector-based learning methods, one could treat...
Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, ...
Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for i...
Shashi Shekhar, Paul R. Schrater, Ranga Raju Vatsa...
This article presents a method aiming at quantifying the visual similarity between two images. This kind of problem is recurrent in many applications such as object recognition, i...