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例样本

例样本

例样本

例样本

1

The computational analysis has showed that, KNNFL behaves robustly even if the number of positive training examples is extremely small .

通过分析可以看到，KNNFL 即使在正例样本非常稀少的情况下仍然具有鲁棒性的表现。

2

And then , with the help of related Wikipedia pages, more positive documents are extracted from the unlabeled documents.

其次，通过得到的维基百科文档从未标记样本中抽取相关的正例样本；

3

The researchers examined 1, 126 molecules produced by the body in a total of 262 samples of tissue, blood or urine .

研究者在 262 例样本（血液或尿液）中检测了 1,126 个分子。

4

Finally , this paper trains a text classifier with these positive documents and unlabeled documents.

最后，基于抽取出的正例样本和未标记样本构造分类器。

5

And finally , this paper trains a text classifier with these positive documents and unlabeled documents.

最后，基于抽取出的正例样本和未标记样本建立分类器。

6

Results Among the 28 specimens , 22 cases(78. 6%) were confirmed accurate through serological measure of fetal blood after birth.

结果：28 例样本中，产前检查能正确显示胎儿血型的有 22 例，总准确率为 78 .

7

Results Based on 30 samples , test proved that the new combined approach improved the signaltonoise ratio , accuracy and reliability .

结果使用 30 例样本检测算法性能，证明联合算法能够提高信噪比，对特征点的定位准确可靠。

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