Towards Making Unlabeled Data Never Hurt
It is usually expected that learning performance can be improved by exploiting unlabeled data, particularly when the number of labeled data is limited. However, it has been reported that, in some cases existing semi-supervised learning approaches perform even worse than supervised ones which only us...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 37(2015), 1 vom: 01. Jan., Seite 175-88 |
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| Format: | Article en ligne |
| Langue: | English |
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2015
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article Research Support, Non-U.S. Gov't |
| Accès en ligne |
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