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Weighted random subspace method for high dimensional data classification
High dimensional data, especially those emerging from genomics and proteomics studies, pose significant challenges to traditional classification algorithms because the performance of these algorithms may substantially deteriorate due to high dimensionality and existence of many noisy features in the...
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| Hauptverfasser: | , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
2009
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3170928/ https://ncbi.nlm.nih.gov/pubmed/21918713 |
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