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-折交叉确认
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  5-fold cross-validation
     To serve this purpose,we use a grid search technique using 5-fold cross-validation to find out the optimal parameter values of various kernel function of SVM.
     为了达到这个目标及保证可靠性,研究中使用网格5-折交叉确认来寻找不同核函数的最优参数.
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  相似匹配句对
     C5, clinoid;
     C5
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     (5) it could improve micro-circulation
     (5
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     Its accuracy can be estimated by K-fold cross-validation.
     决策树算法的准确率可以由K-交叉确认进行检验。
短句来源
     To serve this purpose,we use a grid search technique using 5-fold cross-validation to find out the optimal parameter values of various kernel function of SVM.
     为了达到这个目标及保证可靠性,研究中使用网格5-交叉确认来寻找不同核函数的最优参数.
短句来源
     Finally,5 proteins were identified.
     有5个蛋白质最终鉴定确认
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  5-fold cross-validation
A 5-fold cross-validation technique was used to evaluate the performance of different classifiers.
      
Both of the optimizations were done on a validation set and the optimal set of hyperparameters was selected by 5-fold cross-validation.
      
Each classifier was tested 10 times on each dataset with the use of 5-fold cross-validation.
      
Here we investigate this bias, and also consider 5-fold cross-validation as an alternative strategy.
      
Standard 5-fold cross-validation is performed to find optimal parameters for each model.
      
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Rough set modeling method is a new and important method in the welding process modeling.In this method,discretization is a necessary and important approach.To select proper method from lots of discretization method,proper evaluation criterions in welding field were discussed and decided.Furthermore,the same data sampled from mild steel pulsed gas tungsten arc welding were used as the modeling data,the same rough set modeling method except the discretization approach was used to obtain the rough set model,and...

Rough set modeling method is a new and important method in the welding process modeling.In this method,discretization is a necessary and important approach.To select proper method from lots of discretization method,proper evaluation criterions in welding field were discussed and decided.Furthermore,the same data sampled from mild steel pulsed gas tungsten arc welding were used as the modeling data,the same rough set modeling method except the discretization approach was used to obtain the rough set model,and the ten folds cross validation method was used to compare the discretization methods' effect mainly on the rough set model's prediction ability.The result shows that the entropy based discretization method is better in welding field.At last,the selection of parameters in the entropy based method was discussed.

粗糙集建模方法是一种新的且重要的焊接过程建模方法,离散化是粗糙集建模方法中一个必要且重要的步骤.针对已有离散化方法众多、难以取舍的特点,讨论并确立了焊接建模过程中离散化方法的选择标准.以低碳钢的脉冲钨极氩弧焊为背景,针对相同的建模数据,采用除离散化外相同的粗糙集建模步骤,并以十折交叉确认法为验证方法,对比不同离散化方法对焊接粗糙集模型的预测能力的影响.实验表明,基于熵的离散化方法更加适合焊接过程的粗糙集建模.最后,探讨了基于熵的离散化方法的参数选择.

As credit industry has expanded rapidly over last several years,credit scoring models have drawn a lot of research interests in previous literature.Recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones.This paper applies support vector machines(SVMs) to the credit scoring prediction problem in an attempt to suggest a new model with better classification accuracy.To serve this purpose,we use a grid search technique using 5-fold cross-validation...

As credit industry has expanded rapidly over last several years,credit scoring models have drawn a lot of research interests in previous literature.Recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones.This paper applies support vector machines(SVMs) to the credit scoring prediction problem in an attempt to suggest a new model with better classification accuracy.To serve this purpose,we use a grid search technique using 5-fold cross-validation to find out the optimal parameter values of various kernel function of SVM.In addition,to evaluate the prediction accuracy of SVM,we compare its performance with those of linear discriminant analysis(LDA),logistic regression analysis(Logit),K-nearest neighbours(K-NN),classification and regression tree and neural networks(ANN).The experiment results show that SVM have a very good prediction accuracy.

运用基于支持向量机理论试图建立一个新的个人信用评估预测方法,以期取得更好的预测分类能力.为了达到这个目标及保证可靠性,研究中使用网格5-折交叉确认来寻找不同核函数的最优参数.为了进一步评价SVM分类准确性,我们在本文最后对SVM方法与线性判别分析,Logistic回归分析,最近邻,分类回归树及神经网络进行了比较,结果表明,SVM有很好的预测效果.

>=SVM is a new classification technology based on the theory of statistic learning and idea of structural risk minimization.In many applications, SVM has been shown to provide higher performance than traditional learning machines and has been introduced as powerful tools for solving problems of classification. In this paper, we apply SVM into discriminant analysis of DNA sequences. Some concluding remarks and contrast to other traditional statistic discriminant analysis method are given in the end.

本文利用SVM方法对DNA序列样本进行判别分类,将编码区序列与非编码区序列作为两个总体,将输入空间映射到高维空间,以求在高维空间中问题线性可分或接近线性可分。再得到原空间的判别曲面,用所得到的判别曲面对编码区与非编码区有差异的特征序列进行划分。为了评估SVM方法分类的准确率,我们采用统计分析中的k-折交叉确认方法对样本数据进行了训练样本与测试,并将其结果与传统的判别分析法进行对照。实际处理结果表明在小样本情况下,SVM方法克服传统的判别分析方法对总体分布要求的局限性,分类效果优于传统的判别分析方法。

 
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