Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
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With increasing amounts of data being generated by businesses and researchers there is a need for fast, accurate and robust approach for enterprise decision. Because the final goal of text categorization is to support decision, the web text categorization must adapt the dynamic change over the time as the web text documents increase rapidly. With the advent of grid technologies, the idea of Grid-based Open DSS (GBODSS) is becoming a reality. In this study, an approach of web text categorization based on Support Vector Machines (SVMs) in GBODSS framework is developed to support enterprise decision making. The experiments were conducted on different configurations of grid network and computation time was recorded for each operation. We analyzed our result with various grid configurations and it shows speed up of computation time is almost super linear. The experiment results reported here clearly show the potential of GBODSS while highlighting the need for further research into the decision support system.