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A new semi-supervised approach for Chinese relation extraction (RE) over constantly growing and edgeless web data is introduced in this paper. Existing semi-supervised approaches have the better improvement potential while lacking syntactic structure and semantic meaning of a sentence and unsuitable to loosely structured Chinese sentences. To follow their basic procedures as well as covering their...
Branch and bound for semi-supervised support vector machines as an exact, globally optimal solution is useful for benchmarking different practical S3VM implementation. But, global optimization can be computationally very demanding. Parallel implementation of the algorithm enables us to reduce computational time significantly and to solve larger problems. Focusing on the time consuming problem of BBS3VM,...
Support vector machine (SVM) has become a popular classification tool but one of its disadvantages is large memory requirement and computation time when dealing with large datasets. Parallel methods have been proposed to speed up the process of training SVM. An improved cascade SVM training algorithm is proposed, in which multiple SVM classifiers are applied. The support vectors are obtained by feeding...
Support vector machine (SVM) is originally developed for binary classification problems. In order to solve practical multi-class problems, various approaches such as one-against-rest (1-a-r), one-against-one (1-a-1) and decision trees based SVM have been presented. The disadvantages of the existing methods of SVM multi-class classification are analyzed and compared in this paper, such as 1-a-r is...
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