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2008 International Conference on BioMedical Engineering and Informatics
The Support Vector Machine Classification System for Patent Document Information Importance Analysis
May 27-May 30
ISBN: 978-0-7695-3118-2
This study proposed a novel two-stage process of integrating support vector machine with expert screening technique to develop an automatic patent categorization system with high accuracy and high validity. The approach is tested on a real world case—the search history involving 264 patent documents of semiconductor equipment components. A 100% patent classification accuracy via the description portion of the patent documents was achieved using our proposed two-stage approach. The results showed that the proposed approach performed well in the real-world case of patent classification. The description field of the patent document was more than adequate for patent classification.
Index Terms:
Patent Classification, Support Vector Machine, Expert Screening
Citation:
Chih-Hung Wu, Yun Ken, Tao Huang, "The Support Vector Machine Classification System for Patent Document Information Importance Analysis," bmei, vol. 1, pp.375-379, 2008 International Conference on BioMedical Engineering and Informatics, 2008
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