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Soft-margin support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter.(More)
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This study aims at combining Bayesian networks with item ordering theory to build the Bayesian networks based adaptive test System and explores the efficiency of using this computerized adaptive diagnostic test system in practical instruction. The domain content chosen is the rounding and estimating with decimals unit. The results show that the proposed(More)
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