Self-Taught Learning
From Ufldl
(→On the terminology of unsupervised feature learning) |
(→On the terminology of unsupervised feature learning) |
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ones are motorcycles), then we could use this form of unlabeled data to | ones are motorcycles), then we could use this form of unlabeled data to | ||
learn the features. This setting---where each unlabeled example is drawn from the same | learn the features. This setting---where each unlabeled example is drawn from the same | ||
| - | distribution as your labeled examples---is sometimes called the | + | distribution as your labeled examples---is sometimes called the semi-supervised |
| - | setting. In practice, we | + | setting. In practice, we often do not have this sort of unlabeled data (where would you |
get a database of images where every image is either a car or a motorcycle, but | get a database of images where every image is either a car or a motorcycle, but | ||
just missing its label?), and so in the context of learning features from unlabeled | just missing its label?), and so in the context of learning features from unlabeled | ||
data, the self-taught learning setting is much more broadly applicable. | data, the self-taught learning setting is much more broadly applicable. | ||
