ACAS: automated construction of application signatures
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
A Bayesian network approach to semantic labelling of text formatting in XML corpora of documents
UAHCI'07 Proceedings of the 4th international conference on Universal access in human-computer interaction: applications and services
A bayesian approach to classify conference papers
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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This paper discusses the use of the bayesian network model for a classification problem related to the document image understanding field. Our application is focused on logical labeling in documents, which consists in assigning logical labels to text blocks. The objective is to map a set of logical tags, composing the document logical structure, to the physical text components. We build a bayesian network model that allows this mapping using supervised learning, and without imposing a priori constraints on the document structure. The learning strategy is based partly on genetic programming tools. A prototype has been implemented, andtested on tables of contents found in periodicals and magazines.