Xiao-Rong Lin

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To handle problems created by large data sets, we propose a method that uses a decision tree to decompose a given data space and train SVMs on the decomposed regions. Although there are other means of decomposing a data space, we show that the decision tree has several merits for large-scale SVM training. First, it can classify some data points by its own(More)
—In this paper, we propose a method for classifying textual entities of bilingual documents written in Chinese and English. In contrast to earlier works that performed classification on the level of textlines or documents, we apply our method to the level of textual components, as we must first identify Chinese components before merging them into intact(More)
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