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Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Srinivasa, KG and Venugopal, KR and Patnaik, LM (2007) Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines. In: Intelligent Data Analysis, 11 (5). pp. 481-496.

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Official URL: http://iospress.metapress.com/content/342j2611v05p...

Abstract

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Item Type: Journal Article
Additional Information: copyright of this article belongs to IOS Press
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 07 Jun 2010 09:16
Last Modified: 07 Jun 2010 09:16
URI: http://eprints.iisc.ernet.in/id/eprint/26833

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