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The paper expounds the application of Bayes discriminant analysis in safety evaluation.

论述了多元统计分析方法中的贝叶斯判别分析方法在安全评价中的应用.

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Implementation results reveal that the MTS outperforms traditional discriminant analysis methods.

执行的结果发现MTS的分类结果较传统鉴别分析佳.

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Kernel linear discriminant analysis ( KLDA ) is essentially a nonlinear feature extraction criterion.

核线性判别准则 ( KLDA ) 是一种非线性特征提取准则.

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Sequential discriminant analysis ( SDA ) is a classical method to select variables.

逐步判别分析是变量筛选的经典方法.

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Afternoon be concerned about the amount of energy change, discriminant rebound effort.

后市需关注量能变化, 判别反弹力度.

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Based on uncorrelated discriminant analysis, kernel uncorrelated discriminant analysis is developed.

核不相关鉴别分析是在线性不相关鉴别分析的基础上发展起来的.

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The discriminant analysis has an important significance for orebody classification.

判别分析对研究未知属性矿体的归属有着重要的意义.

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Finally, the discriminant rate close to 90.

最后的判别正确率接近90%.

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Conclusion: Maxium likelihood discriminant method is practical in quantitative diagnosis of syndromes of apoplectic sequela.

结论: 用最大似然判别法探讨中风后遗症中医证候计量诊断方法,有较好的实用价值.

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Independent Component Analysis ( ICA ) and Linear Discriminant Analysis ( LDA ) are two effective methods of feature extraction.

本文提出了一种基于独立成分分析和线性鉴别分析的特征提取方法.

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Statistical techniques , including Multi - Regression, Cluster Analysis, and Discriminant Analysis , were employed to test ten null hypotheses.

本研究以世界银行发展报告书的统计为依据,共选定1970年、1975年 、 1980年 、 1985年、1990年、1995年各国高等教育在学率为分析资料.

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Then, the features of the general discriminant must be relative to the law of structure change.

于是, 得到晋遍判别法的应有特征,其判别式也应有规律,且须与完备风网系列间的结构变化规律相对应.

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Practical potential of principal component analysis ( PCA ) and linear discriminant analysis ( LDA ), training and inspection.

实用可能的主成分分析 ( PCA ) 和线性判别分析 ( LDA ), 训练和检查.

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Linear Discriminant Analysis ( LDA ) is one of the classical and popular Methods: Used for feature extraction.

线性 鉴别 分析是特征抽取中最为经典和广泛使用的方法之一.

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However, the result is unsatisfactory when the correct discriminant rate only lies between 53 - 63 % .

惟其正确区别率仅在 53-63% 之间, 效果并不十分理想.

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