在无指导的情况下，将上述数据库的图像显示在还未受训的回旋网眼前，它能学习识别70%以上的分类。
When a ConvNet with unsupervised pre-training is shown the images from this database it can learn to recognise the categories more than 70% of the time.
但是，无指导学习环境下的属性选择往往无法取得像有指导学习环境下那样令人满意的结果。
However, the result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning.
实验数据分析(无监督学习)可以被用来指导选择合适的学习策略。
Exploratory data analysis (unsupervised learning) may be used to guide the choice of suitable learning strategies.
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