Random forest construction with robust semisupervised node splitting
Random forest (RF) is a very important classifier with applications in various machine learning tasks, but its promising performance heavily relies on the size of labeled training data. In this paper, we investigate constructing of RFs with a small size of labeled data and find that the performance...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 24(2015), 1 vom: 23. Jan., Seite 471-83
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| 1. Verfasser: |
Liu, Xiao
(VerfasserIn) |
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| Weitere Verfasser: |
Song, Mingli,
Tao, Dacheng,
Liu, Zicheng,
Zhang, Luming,
Chen, Chun,
Bu, Jiajun |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2015
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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| Schlagworte: | Journal Article
Research Support, Non-U.S. Gov't |
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