Titlebook: Data Mining; 16th Australasian Co Rafiqul Islam,Yun‘Sing Koh,Zahidul Islam Conference proceedings 2019 Springer Nature Singapore Pte Ltd. 2

Motilin

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Carole Bouchard,Jean-François Omhoverty based on 11 quasi-identifiers, with less than 3% suppression, compared with only 3-anonymity based on no more than 8 quasi-identifiers with far more than 3% suppression commonly reported in literature. Furthermore, our method enabled random forest classifier to achieve 0.996 for AUC and 0.895 for

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彻底明白

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Enthralling

Vertebra

Multiple Support Vector Machines for Binary Text Classification Based on Sliding Window Techniquet vector machines are proposed that can effectively deal with the uncertain boundary and improve predictive accuracy in linear SVM for data having uncertainties. This is achieved by dividing the training documents into three distinct regions (positive, boundary, and negative regions) based on a slid

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