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Shallow Representation Learning Via Kernel PCA Improves QSAR Modelability
Linear models offer a robust, flexible, and computationally efficient set of tools for modeling quantitative structure activity relationships (QSAR), but have been eclipsed in performance by non-linear methods. Support vector machines (SVMs) and neural networks are currently among the most popular a...
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| 出版年: | J Chem Inf Model |
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| 主要な著者: | , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2017
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5942586/ https://ncbi.nlm.nih.gov/pubmed/28727421 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1021/acs.jcim.6b00694 |
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