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。
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。
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。
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。
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。
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。
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。
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。
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