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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Chen, IX., Ho, JC., Yang, CZ. (2005). An Iterative Approach for Web Catalog Integration with Support Vector Machines. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.H. (eds) Information Retrieval Technology. AIRS 2005. Lecture Notes in Computer Science, vol 3689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562382_71
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DOI: https://doi.org/10.1007/11562382_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29186-2
Online ISBN: 978-3-540-32001-2
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