Most text categorization methods require text content of documents that is often difficult to obtain. We consider "Collaborative Text Categorization", where each document...
Class binarizations are effective methods for improving weak learners by decomposing multi-class problems into several two-class problems. This paper analyzes how these methods can...
Recently, a successful extension of Principal Component Analysis for structured input, such as sequences, trees, and graphs, has been proposed. This allows the embedding of discret...
Most traffic management and optimization tasks, such as accident detection or optimal vehicle routing, require an ability to adequately model, reason about and predict irregular an...
Many data mining techniques are these days in use for ontology learning – text mining, Web mining, graph mining, link analysis, relational data mining, and so on. In the current ...
At the International Research and Educational Institute for Integrated Medical Sciences (IREIIMS) project, we are collecting complete medical data sets to determine relationships b...
The availability of techniques for comparing descriptions has many applications in Artificial Intelligence, ranging from description selection to flexible matching, from instance...
Stefano Ferilli, Teresa Maria Altomare Basile, Nic...
Abstract. A clustering method is presented which can be applied to semantically annotated resources in the context of ontological knowledge bases. This method can be used to discov...
Word meaning ambiguity has always been an important problem in information retrieval and extraction, as well as, text mining (documents clustering and classification). Knowledge di...
Henryk Rybinski, Marzena Kryszkiewicz, Grzegorz Pr...