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Recursive Feature Elimination by Sensitivity Testing
There is great interest in methods to improve human insight into trained non-linear models. Leading approaches include producing a ranking of the most relevant features, a non-trivial task for non-linear models. We show theoretically and empirically the benefit of a novel version of recursive featur...
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| 出版年: | Proc Int Conf Mach Learn Appl |
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| 主要な著者: | , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2019
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6887481/ https://ncbi.nlm.nih.gov/pubmed/31799516 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/ICMLA.2018.00014 |
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