来自 | AI公园 作者 | Neeraj varshney
区分半监督学习，监督学习和无监督学习
整个数据集中可用于训练的有标记数据的范围区分了机器学习的这三个相关领域。
我们的目标是学习一个预测器来预测未来的测试数据，这个预测器比单独从有标记的训练数据中学习的预测器更好。
为什么要关注半监督学习
半监督学习的任务举例
-
CIFAR-10 — 它是由10个类的32×32像素的RGB图像组成的数据集，任务是图像分类。通常使用Tiny Images数据集中的随机图像来形成未标记数据集。 -
SVHN — 街景门牌号数据集由真实门牌号的32×32像素的RGB图像组成，任务是分类最中间的数字。它附带一个“SVHN-extra”数据集，该数据集由531,131个额外的数字图像组成，可以用作未标记数据。 -
Text-Classification Tasks — 亚马逊评论数据库，Yelp评论数据集。
总结
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本篇文章来源于: 深度学习这件小事
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