首页> 外文OA文献 >Building Highly Reliable Quantitative StructureActivity RelationshipAQ1: Is the expanded name provided for {siComponents}#x201c;QSAR{siComponents}#x201d; correct? Classification Models Using the Rivality Index Neighborhood Algorithm with Feature Selection

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Building Highly Reliable Quantitative StructureActivity RelationshipAQ1: Is the expanded name provided for {siComponents}#x201c;QSAR{siComponents}#x201d; correct? Classification Models Using the Rivality Index Neighborhood Algorithm with Feature Selection

机译：建立高度可靠的定量结构假期关系和{sicomponents}＃x201c; qsar {sicomponents}＃x201d提供的扩展名称是{sicomponents}＃x201d;正确的？使用具有特征选择的招生索引邻域算法的分类模型

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1. Building Highly Reliable Quantitative Structure-Activity Relationship Classification Models Using the Rivality Index Neighborhood Algorithm with Feature Selection [J] . Luque Ruiz Irene, Angel Gomez-Nieto Miguel Journal of chemical information and modeling . 2020,第1期

机译：建立高度可靠的定量结构 - 活动关系分类模型，使用具有特征选择的招生指数邻域算法
2. Rivality index neighbourhood algorithm with density and distances weighted schemes for the building of robust QSAR classification models with high reliable applicability domain [J] . I. Luque Ruiz, M.á. Gómez-Nieto SAR and QSAR in Environmental Research . 2019,第7a9期

机译：具有密度和距离的Rivality指数邻域算法，用于建立具有高可靠性适用性域的强大QSAR分类模型的构建强大的QSAR分类模型

Building Highly Reliable Quantitative StructureActivity RelationshipAQ1: Is the expanded name provided for {siComponents}#x201c;QSAR{siComponents}#x201d; correct? Classification Models Using the Rivality Index Neighborhood Algorithm with Feature Selection

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