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- The Feature Selection page consists of two panes. “功能选择”页有两个窗格。
- Set this value to 0 to turn off feature selection. 如果将此值设置为0,则表示关闭功能选择。
- You should now see the feature selection screen. 现在您应该看到特性选择屏幕了。
- Hi, everyone, Here is a question about feature selection. 有关特征筛选的一个问题。
- On the Feature Selection page, expand Client Components. 在“功能选择”页上,展开“客户端组件”。
- On the Feature Selection page, expand Database Services. 在“功能选择”页上展开“数据库服务”。
- PRN combined feature selection algorithm is proposed. 并提出改进的PRN组合特征选择算法。
- The chief obstacles to feature selection are noise and sparseness. 本文介绍了一种基于类别特征域的特征选择方法。
- Facial feature selection method based on SVM RFE[J]. 引用该论文 李伟红;龚卫国;陈伟民;梁毅雄;尹克重.
- Feature selection and classification are the key points in BCI research. 特征提取和分类是脑-机接口的关键。
- Defines the number of input attributes that the algorithm can handle before it invokes feature selection. 定义算法在调用功能选择之前可以处理的输入属性数。
- Defines the number of output attributes that the algorithm can handle before it invokes feature selection. 定义算法在调用功能选择之前可以处理的输出属性数。
- On the Feature selection page, select the program features to install. 在“功能选择”页上,选择要安装的程序功能。
- This paper employs feature selection theory and pattern aggregation theory to reduce feature space dimension. 应用特征选取和模式聚合理论以降低特征空间维数。
- The Feature Selection dialog in SQL Server Setup allows you to install Engine Components and Client Components. 通过SQL Server安装程序中的“功能选择”对话,可以安装引擎组件和客户端组件。
- Feature selection is one of key factors that influences the development of text classification. 摘要特征选择是影响文本分类技术发展的关键因素之一。
- This paper presents a feature selection method based on genetic algorithm GA) and linear discriminant analysis LDA). 摘要本文提出了一种基于遗传算法的基因微阵列数据特征提取方法。
- Feature selection is a valid method to reduce the dimension of text vector in automatic text categorization system. 在自动文本分类系统中,特征选择是有效降低文本向量维数的一种方法。
- At present the key problem about PFGs still is the plant feature selection applying to classication of PFGs. 目前功能群研究中最核心的问题仍在于决定植物功能群划分的植物特征的选择上。
- The result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning. 无监督学习环境下的特征选择往往无法取得像有监督学习环境下那样令人满意的效果。
