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Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises
Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values...
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| 出版年: | IEEE Trans Pattern Anal Mach Intell |
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| 主要な著者: | , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2018
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6050136/ https://ncbi.nlm.nih.gov/pubmed/29994560 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TPAMI.2018.2794470 |
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