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 trains 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)
To handle problems created by large data sets, we propose a method that uses a decision tree to decompose a data space and trains 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 means,(More)
Biological and artificial molecules and assemblies capable of supramolecular recognition, especially those with nucleobase pairing, usually rely on autonomous or collective binding to function. Advanced site-specific recognition takes advantage of cooperative spatial effects, as in local folding in protein-DNA binding. Herein, we report a new(More)
Handwritten signature is widely used for human identity authentication in daily life. In order to improve the recognition accuracy and promote the success rate, it is necessary to preprocess the signature image. In this paper, the signature image preprocessing included smooth filtering, image binaryzation, signature area acquisition, and edge thinning.(More)
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