Browsing by Author "Stephane Bressan"

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    Association Rules Mining for Name Entity Recognition
    (2003-06-01T00:00:00Z) Indra Budi; Stephane Bressan
    We propose a new name entity class recognition method based on association rules. We evaluate and compare the performance of our method with the state of the art maximum entropy method. We show that our method consistently yields a higher precision at a competitive level of recall. This result makes our method particularly suitable for tasks whose requirements emphasize the quality rather than the quantity of results.
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    Continuous Naive Bayesian Classification
    (2003-06-01T00:00:00Z) Vinsensius Berlin Vega S. N.; Stephane Bressan
    The most common model of machine learning algorithms involves two life-stages, namely the learning stage and the application stage. The cost of human expertise makes difficult the labeling of large sets of data for the training of machine learning algorithms. In this paper, we propose to challenge this strict dichotomy in the life cycle while addressing the issue of labeling of data. We discuss a learning paradigm called Continuous Learning. After an initial training based on human-labeled data, a Continuously Learning algorithm iteratively trains itself with the result of its own previous application stage and without the privilege of any external feedback. The intuitive motivation and idea of this paradigm are elucidated, followed by an explanation on how it differs from other learning models is laid out. Finally, empirical evaluation of Continuous Learning applied to the Naive Bayesian Classifier for the classification of newsgroup articles of a well-known benchmark is presented.
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    Informal Proceedings of The First VLDB Workshop on Efficiency and Effectiveness of XML Tools, and Techniques (EEXTT2002)
    (2002-08-01T00:00:00Z) Mong Li Lee; Stephane Bressan; Akmal Chaudhri
    With XML potentially becoming the standard for data exchange on the Internet, a variety of XML management systems (XMLMS) differing widely in terms of expressive power and performance are becoming available. The majority of the XML management systems are legacy systems (mostly relational database systems) extended to load, query, and publish data in XML format. A few are native XMLMS and capture almost all the characteristics of XML data representation. Yet a large number of new techniques are being tuned or devised for the management of XML data. In this workshop we propose to focus on the evaluation of the performance, effectiveness and efficiency, of XMLMS systems, tools and techniques. The first VLDB workshop on efficiency and effectiveness of XML tools and techniques hosts papers on various aspect of the management of XML data and of the XML data management systems such as query and manipulation languages, modeling and integration, and storage.