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In this paper we propose a new methodology for link-based document classification based on probabilistic classifiers and Bayesian networks. We also report the results obtained of its application to the XML Document Mining Track of INEX΄09. [ABSTRACT FROM AUTHOR]

Robust rotation invariance has been a matter of great interest in many applications which use low-level features such as textures. In this paper, we propose a method to analyze and capture visual patterns from textures regardless their orientation. In order to achieve rotation invariance, visual texture patterns are locally described as one-dimensional patterns by appropriately steering the Cartesian Hermite coefficients. Experiments with two datasets from the Brodatz album were performed to evaluate orientation invariance. High average precision and recall rates were achieved by the proposed method. [ABSTRACT FROM AUTHOR]

This paper exposes the results of our participation in the Document Mining track at INEX΄07. We have focused on the task of classification of XML documents. Our approach to deal with structured document representations uses classification methods for plain text, applied to flattened versions of the documents, where some of their structural properties have been translated to plain text. We have explored several options to convert structured documents into flat documents, in combination with two probabilistic methods for text categorization. The main conclusion of our experiments is that taking advantage of document structure to improve classification results is a difficult task. [ABSTRACT FROM AUTHOR]

This paper exposes the results of our participation at INEX΄07 in the AdHoc track and the comparison of these results with respect to the ones obtained last year. Three runs were submitted to each of the Focused, Relevant In Context and Best In Context tasks, all of them obtained with Garnata, our Information Retrieval System for structured documents. As in the past year, we use a model based on Influence Diagrams, the CID model. The result of our participation has been better than the last year and we have reached an acceptable position in the ranking for the three tasks. In the paper we describe the model, the system and we show the differences between our systems at INEX΄06 and INEX΄07, which make possible to get a better performance. [ABSTRACT FROM AUTHOR]

We propose a method which, given a document to be classified, automatically generates an ordered set of appropriate descriptors extracted from a thesaurus. The method creates a Bayesian network to model the thesaurus and uses probabilistic inference to select the set of descriptors having high posterior probability of being relevant given the available evidence (the document to be classified). We apply the method to the classification of parliamentary initiatives in the regional Parliament of Andalucía at Spain from the Eurovoc thesaurus. [ABSTRACT FROM AUTHOR]