Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
On-line new event detection, clustering, and tracking (information retrieval, internet)
On-line new event detection, clustering, and tracking (information retrieval, internet)
| Hi-index | 0.00 |
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.