Abstract
This paper describes a new method for detecting events. The method utilizes density function to initialize cluster centers so as to get center points objectively, it can be used in both on-line detection and retrospective detection, and the quantity of clusters is affected little by the order in which the news stories are processed. This paper deals with event tracking with transductive support vector machine (TSVM). TSVM takes into account a particular test set and tries to minimize misclassifications of just those particular examples. The problem of effective density radius selection is discussed, and the performance differences between the event detection and tracking methods proposed in this paper and other methods are compared. The experimental results indicate that the methods proposed by this paper are effective and advanced.
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© 2008 Springer-Verlag Berlin Heidelberg
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Lei, Z., Liao, J., Li, D., Wu, L. (2008). Event Detection and Tracking Based on Improved Incremental K-Means and Transductive SVM. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_105
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DOI: https://doi.org/10.1007/978-3-540-85984-0_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
Online ISBN: 978-3-540-85984-0
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