An iterative approach for web catalog integration with support vector machines

Authors:
Ing-Xiang Chen;Jui-Chi Ho;Cheng-Zen Yang
Affiliations:
Department of Computer Science and Engineering, Yuan Ze University, Taiwan, R.O.C.;Department of Computer Science and Engineering, Yuan Ze University, Taiwan, R.O.C.;Department of Computer Science and Engineering, Yuan Ze University, Taiwan, R.O.C.
Venue:
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Year:
2005

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Abstract

Web catalog integration is an emerging problem in current digital content management. Past studies show that more improvement on integration accuracy can be achieved with advanced classifiers. Because Support Vector Machine (SVM) has shown its supremeness in recent research, we propose an iterative SVM-based approach (SVM-IA) to improve the integration performance. We have conducted experiments of real-world catalog integration to evaluate the performance of SVM-IA and cross-training SVM. The results show that SVM-IA has prominent accuracy performance, and the performance is more stable.