• The method is suitable for large scale and high dimension data classifying problem.

    该方法适用于大规模及高维属性的数据分类问题。

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  • A combined optimization decision tree algorithm suitable for a large scale and high dimension data-base is presented.

    提出了一种适合于大规模高维数据库的组合优化决策树算法。

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  • The effectiveness and efficiency of the algorithms have been shown by experimental results, especially for the high dimension data warehouses.

    实验证明了一系列算法的效率和有效性,尤其适合数据仓库中的高维数据集。

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  • Compared with some other modeling methods, such as ANFIS, the proposed model is of less computation, higher accuracy, especially for high dimension data modeling.

    与其他建模方法相比,如anfis,模糊树模型计算量小,精度高,尤其在高维数据建模中更为明显。

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  • The query of High dimension data attracts more and more attention.

    目前,高维数据的快速检索问题已经受到越来越多的关注。

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  • The high generalization ability of Support Vector Machine (SVM) makes it especially suitable for the classification of high-dimension data such as term-document.

    支持向量机(SVM)高度的泛化能力使它特别适用于高维数据例如文档的分类。

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  • Mixtures of factor analyzers, is a nonlinear tool for high-dimension data.

    混合因子分析模型是一种非线性的分析高维数据的工具。

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  • Adaptive Random Testing (ART) is an enhanced version of Random Testing (RT). There are two factors that restrict the performance of ART, high-dimension data and distance metric.

    针对自适应随机测试(ART)存在的高维和距离度量问题,提出一种改进的软件自适应随机测试策略。

    youdao

  • Adaptive Random Testing (ART) is an enhanced version of Random Testing (RT). There are two factors that restrict the performance of ART, high-dimension data and distance metric.

    针对自适应随机测试(ART)存在的高维和距离度量问题,提出一种改进的软件自适应随机测试策略。

    youdao

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