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SVM-RFE: selection and visualization of the most relevant features through non-linear kernels
BACKGROUND: Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited because SVM was not designed to evaluate importance...
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| 出版年: | BMC Bioinformatics |
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| 主要な著者: | , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
BioMed Central
2018
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6245920/ https://ncbi.nlm.nih.gov/pubmed/30453885 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-018-2451-4 |
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