Semi-Supervised Boosting Using Similarity Learning Based on Modular Sparse Representation With Marginal Representation Learning of Graph Structure Self-Adaptive
The purpose of semi-supervised boosting strategy is to improve the classification performance of one given classifier for a large number of unlabeled data. In the semi-supervised boosting strategy, the unlabeled samples are assigned for pseudo labels according to similarities between the labeled sam...
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| Asıl Yazarlar: | , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
IEEE
2020-01-01
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| Seri Bilgileri: | IEEE Access |
| Konular: | |
| Online Erişim: | https://ieeexplore.ieee.org/document/9220775/ |
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