- 更多网络例句与误分类相关的网络例句 [注：此内容来源于网络，仅供参考]
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The misclassification in the second quarter of 2000 was reflected in Delphi's quarterly and annual financial statements.
此次误分类发生在2000年二季度，并反映在 Delphi 公司该季度及当年的财务报表中。
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Experimental results show that CS-SVM greatly reduces the average misclassification cost.
试验结果表明：相比于SVM，CS-SVM大大降低测试集上的平均误分类代价。
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ETA almost always produces the lowest misclassification cost, and is least sensitive to the misclassification cost ratio.
ETA几乎总是得到最低的误分类代价，而且它对误分类代价率最不敏感。
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On the basis of CS-SVM, a general cost-sensitive classification algorithm wrapping different misclassification costs of each class sample is proposed.
基于CS-SVM的思路，提出一个通用的嵌入误分类代价的代价敏感分类算法G-CSC。
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Under some restrictions, firstly, the functional equivalence between Misclassification Cost-sensitive SVM and rule-based FIS is proposed.
在一定约束条件下，本文提出并证明误分类代价敏感SVM和一类基于规则的FIS的函数等效性定理。
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In this method,rule intensity is defined according to the number of misclassified training samp...
该算法根据误分类训练样本的数量定义规则强度。
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In the traditional AdaBoost method,the weights are updated based on the weak classifier's overall error rate, this article proposes one kind of the method which unifies the level of difference between the threshold and the feature value of the weak classifier with the weak classifier's overall error rate. Compared to the method which only based on the overall classification error rate to update the weights,this method can achieve higher detection rate while reduces the mistaken-detection rate.
在传统AdaBoost方法中，权重是根据弱分类器的整体错误率来更新的，本文提出了一种将弱分类器的阈值与特征值相差程度和弱分类器的整体错误率相结合来更新样本权重的方法，相比较只根据整体分类错误率来进行权重更新的方法，这种方法可以在降低误检率的同时达到较高的检测率。
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In this method,rule intensity is defined according to the number of misclassified training samples.
该算法根据误分类训练样本的数量定义规则强度。
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To avoid this, the priors can be specified, where the anomaly probability is artificially increased (up to 0.5 or even greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is adjusted properly.
为了避免这一点，先验就必须被指定，异常情况发生的概率需要人为的增加（增加到0.5甚至更高），这样误分类的异常情况的权重就会变大，树也能够得到适当的调整。
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When one rule is generated, boosting mechanism is used to change the weights of the training instances so as to mine new rules that are focused on currently uncovered or misclassified instances.
Boosting机制用于调整训练数据的权值，使得新生成规则集中于当前未被覆盖或误分类的数据实例。
- 更多网络解释与误分类相关的网络解释 [注：此内容来源于网络，仅供参考]
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misclassification：分错
miscirculatemisroutemisrouted 误转 | misclassification 分错 | misclassification 错分类
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misclassification：分错 错分类
miscirculate 误转 | misclassification 分错 错分类 | misclosure 闭合差
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misclassification bias：错误分类偏倚
控制方法: 控制方法二,信息偏倚:(information bias)又称测量偏倚或观察偏倚,来自于测量或资料收集方法问题,使获取的资料存在系统误 信息偏倚: 测量偏倚或观察偏倚, 自于测量或资料收集方法问题, 信息偏倚 测量偏倚或观察偏倚 错误分类偏倚(misclassification bias).