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Extracting Positive and Negative Association Classification Rules from RBF Kernel

机译：从RBF核中提取正负关联分类规则

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Recently, building associative classifiers by miming association rules is a hot research problem. As negative association rules also help to understand the data, in this paper, we present our InterRBF algorithm, which expands RBF kernel into its Maclaurin

机译：最近，通过模仿关联规则来建立关联分类器是一个热门的研究问题。由于负关联规则也有助于理解数据，因此在本文中，我们介绍了InterRBF算法，该算法将RBF内核扩展为Maclaurin

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《Convergence Information Technology (ICCIT), 2007 International Conference on 》|2007年|P.1285-1291|共7页
会议地点 Gyeongju-si GyeongbukKorea
作者
Quanzhong Liu; Yang Zhang; Zhengguo Hu;
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data mining; pattern classification; support vector machines; InterRBF algorithm; RBF kernel; SVM classification model; negative association classification rules; positive association classification rules; support vector machines;

机译：数据挖掘;模式分类支持向量机； InterRBF算法； RBF内核； SVM分类模型；否定联想分类规则；正联想分类规则；支持向量机;

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机译：借助积极和消极的规则提高分类准确性
2. ENHANCING CLASSIFICATION ACCURACY WITH FREQUENT POSITIVE AND NEGATIVE RULES [J] . ANIS SUHAILIS ABDUL KADIR, AZURALIZA ABU BAKAR, ABDUL RAZAK HAMDAN Journal of Theoretical and Applied Information Technology . 2014 ,第3期

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4. Extracting Positive and Negative Association Classification Rules from RBF Kernel [C] . Quanzhong Liu, Yang Zhang, Zhengguo Hu International Conference on Convergence Information Technology . 2007

机译：从RBF内核提取正面和负关联分类规则
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Extracting Positive and Negative Association Classification Rules from RBF Kernel

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