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-折交叉验证
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  相似匹配句对
     10?
     10 ;
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     10. We consider that the formula Mim.
     10
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     Generation, parse and verification of cross certificate.
     交叉证书的生成、解析与验证
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     Lastly, the validity of the model is also testified by the cross-validation method.
     最后,利用交叉检验法验证该模型的正确性。
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     Sarazin proved that the crossing number of P(10,4) is four.
     Sarain证明了P(10,4)的交叉数是4。
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  10-fold cross validation
The employed expert system has achieved 100% classification accuracy using a 10-fold Cross Validation (CV) method.
      
Accuracies and reported standard deviations are based on a 10-fold cross validation.
      
Accuracy values for each information source is calculated using 10-fold cross validation.
      
Accuracies of the See5, neural network, and Naive Bayes algorithms in a 10-fold cross validation.
      
A random sub-sampling procedure known as 10-fold cross validation has been developed to minimize any estimation bias.
      
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In this paper, feature selection technique is introduced in optimizing the features of underwater targets. According to the distance measure, the feature subset is automatically selected by the feature selection technique. And the selected feature subsets are evaluated by the 10-folds cross validation. Analysis of experimental results for three different classes of targets shows that the feature selection technique is feasible to select feature subset of the underwater targets, which is useful for the classification...

In this paper, feature selection technique is introduced in optimizing the features of underwater targets. According to the distance measure, the feature subset is automatically selected by the feature selection technique. And the selected feature subsets are evaluated by the 10-folds cross validation. Analysis of experimental results for three different classes of targets shows that the feature selection technique is feasible to select feature subset of the underwater targets, which is useful for the classification task. At the same time, this technique improves the correctness of the classifier.

本文将模式识别中的特征选择方法用于水下目标特征的优化选择。用距离测度作为选择的准则,对提取的三类舰船噪声的高维组合特征进行自动选择,并用10-折交叉验证方法对选择出的特征子集进行评估,分析结果说明该方法能从原始的特征集中选择出有利于分类的特征子集,提高了水下目标分类的正确率。

In order to select a good hypothesis(or model) from a collection of possible models,one has to assess the generalization performance of the hypothesis which returned by a learner that is bound to use some particular model.Several methods for estimating the generalization error of the hypotheses with least test error in the model are intro-duced in this paper,and the advantages and disadvantages of the error estimators are also analyzed in detail.The experi-mental results show different value obtained from cross-validation,RM-bounds...

In order to select a good hypothesis(or model) from a collection of possible models,one has to assess the generalization performance of the hypothesis which returned by a learner that is bound to use some particular model.Several methods for estimating the generalization error of the hypotheses with least test error in the model are intro-duced in this paper,and the advantages and disadvantages of the error estimators are also analyzed in detail.The experi-mental results show different value obtained from cross-validation,RM-bounds and εα-estimator algorithms.The results also show that different problems can choose variant error estimation functions to predict the optimal parameters in the selected model.

不论对分类问题还是回归问题,在构造实际可行的寻找决策函数f(x)的学习算法时,首先要有一个评价f(x)好坏的标准。而评价一个决策函数的性能时,一般是利用样本集估计其在检验集上推断时发生的错误率。给出了几个错误率估计算法,并详细分析了各估计函数的优缺点,最后的实验结果给出了从k-折交叉验证、RM-bounds和εα-estimator函数中预测出的测试错误率,进一步说明了不同的数据集可以选择不同的风险评估算法来预测出所选模型的最优参数。

【Objective】The quality of field seed cottons is assessed objectively.【Method】According to Chinese government grading standards of field seed cotton,grading models of field sampling cottons were developed based on their image features,including size and colors. 【Result】 Correlation analysis showed that the correlations among image features as well as grades of seed cotton quality were significant after image intensity correction. Based on 10-fold cross-validation,Bayes-criterion discrimination results showed...

【Objective】The quality of field seed cottons is assessed objectively.【Method】According to Chinese government grading standards of field seed cotton,grading models of field sampling cottons were developed based on their image features,including size and colors. 【Result】 Correlation analysis showed that the correlations among image features as well as grades of seed cotton quality were significant after image intensity correction. Based on 10-fold cross-validation,Bayes-criterion discrimination results showed that grading models of field sampling cottons were developed with accuracy of 75.00% to 92.86%,with average of 83.20%. Based on a rule of “one-standard error”,a better grading model of field sampling cottons was investigated with a generalization accuracy of 89.11% on an independent-model data,and 1-3 grade of seed cotton quality were recognised with an accuracy of 100%【.Conclusion】 It is feasible to recognise grades of seed cotton quality by using machine vision technologies,which helps to improve the accuracy of grading models of field sampling cottons.

【目的】客观评价田间籽棉质量。【方法】依据中国籽棉品级分级标准,基于机器视觉技术选取棉花尺寸、色泽特征建立田间籽棉品级抽样分级模型。【结果】相关分析表明:亮度修正后,图像特征与籽棉品级之间相关显著。贝叶斯判别分析结果表明:基于10折交叉验证建立的籽棉品级判别模型的识别率在75.00%~92.86%之间,模型的平均识别率达83.20%。基于“1个标准误差”规则选取较好的贝叶斯判别模型,它在独立数据集上的泛化精度达89.11%,其中,前3级籽棉的识别率均达到100%。【结论】基于机器视觉技术识别籽棉品级是可行的,有利于提高籽棉品级抽样分级模型精度。

 
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