标签：scikit-learn 工程应用 semi-supervised 半监督学习 机器学习
参考：http://scikit-learn.org/stable/modules/label_propagation.html
The semi-supervised estimators insklearn.semi_supervised are
able to make use of this additional unlabeled data to better capture the shape of the underlying data distribution and generalize better to new samples. These algorithms can perform well when we have a very small amount of labeled points and a large amount
of unlabeled points.
Unlabeled entries in y：It is important to assign an identifier to unlabeled points along with the labeled data when training
the model with the fit method.
The identifier that this implementation uses is the integer value 
.
有时间翻译：
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scikit-learn（工程中用的相对较多的模型介绍）：1.14. Semi-Supervised
标签：scikit-learn 工程应用 semi-supervised 半监督学习 机器学习
原文地址：http://blog.csdn.net/mmc2015/article/details/47333839
