Feature selection of stored food insect images.
储粮昆虫图像特征优选。
In the Feature Selection screen, select the following.
在Feature Selection界面中，选择以下项。
So, it is necessary to study feature selection algorithms.
所以，对特征选择算法的研究是十分必要的。
Feature reduction can generally fall into feature extraction or feature selection.
特征约简可通过特征提取或特征选择实现。
Feature selection can remove redundant features to enhance the effect of diagnosis.
特征选择可以去除原始特征中的冗余特征，提高诊断精度和诊断效率。
Data pre-processing, data reduction, feature selection, and feature transformation.
数据预处理，数据压缩，特征选择与特征变换。
Feature selection tended to favor necessary improvements over desirable improvements.
他们更多的选择必须的特性，而不是可拥有的特性。
A furthermore experiment proved that the combined feature selection method is effective.
最后通过实验验证组合特征抽取方法的有效性。
Feature selection and feature extraction are common methods for dimensionality reduction.
特征选择和特征抽取是维数约简常用的两种方法。
Accept all the default values during the wizard until you reach the Feature Selection screen.
接受向导中的所有默认值，直至到达Feature Selection界面。
This paper presents a novel feature selection method which is based on class feature domains.
本文介绍了一种基于类别特征域的特征选择方法。
A new feature selection algorithm based on Immune Clonal selection algorithm (ICSA) is proposed.
提出了一种基于免疫克隆选择算法的特征选择方法。
Several feature selection methods were analyzed. An improved mutual information algorithm was proposed.
分析了常用的几种特征选取方法，提出了改进互信息算法。
In this paper, we propose a novel feature selection method based on concept extraction and shielded level.
本文提出了一种新的基于概念抽取和屏蔽层的特征选择方法。
Cross validation, feature selection, quasi-random numbers, and partial least squares in Statistics Toolbox.
在统计工具箱中，增加了交叉验证，特征选择，拟随机数和偏最小二乘法。
On feature selection, document frequency was combined with mutual information, and performance was improved.
特征选择的方法上，结合了文档频数和互信息量，并对他们进行了改进。
In this paper, a posterior-probability-based feature selection algorithm is proposed for imbalanced datasets.
针对不平衡数据集，提出一种基于后验概率的特征选择算法。
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。
This paper presents a method of neural networks feature selection based on data attributes importance ranking.
提出一种基于数据属性重要性排序的神经网络属性选择方法。
Feature selection and parameters optimization of the fault classifier can enhance the fault diagnosis accuracy.
在机械故障诊断中，特征选择和分类器的参数优化都可以提高诊断精度。
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。
The research emphasizes on the feature selection in internal-nodes, and designs a new Distance Difference Function.
论文重点研究了决策树内部结点的特征选择方法，设计了一个新的距离差函数。
In particular methods for confidence estimation and feature selection with Support Vector Machines will be described.
特别是支持向量机的特征选取和信赖度估计方法。
Research on text categorization and information filtering are being done, Multiple Feature Selection Method is presented.
本文对文本分类和信息过滤技术进行了研究，提出了一种多特征选择方法。
Feature selection has proven to be an effective means when dealing with large dimensionality with many irrelevant features.
特征选择在处理具有较多不相关特征的高维数据上已被证明是一种有效的手段。
However, the result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning.
但是，无指导学习环境下的属性选择往往无法取得像有指导学习环境下那样令人满意的结果。
This paper presents two algorithms of linear feature selection used for the study of strong earthquake zoning in Beijing area.
本文介绍两种线性特征选择算法，并用于北京地区强震危险区划的研究。
The techniques of feature extraction, feature selection and design of classifier for passive sonar target recognition are reviewed.
文章对被动声纳目标识别的特征提取、特征选择和分类器设计方面进行了回顾。
Text feature selection is a process of recognizing and deleting redundant information and enhancing training documents cluster quality.
文本特征选择是最大程度地识别和去除冗余信息，提高训练数据集质量的过程。
Feature selection and input dimension reduction are of Paramount im-portance to transient stability assessment based on neural networks.
输入特征选择和输入空间降维是基于神经网络暂态稳定评估的首要问题。
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