Actually the kernel design in the recognition method based on discrete time - frequency representation is a problem of feature selection from the ambiguity functions to reduce feature dimension.
基于离散时频分布的信号识别方法，将时频核设计问题转化为以信号自模糊函数为原始特征的特征选择问题，以实现特征降维和信号识别。
Although there exist numerous feature selection algorithms, new challenging research issues arise for feature selection: from handling a large dimensionality huge number of samples.
尽管出现了大量的特征选择算法，特征选择仍然面临着新的挑战：如何处理高维海量的样本。
A composite feature selection method for reaping the benefits of precision from the wrapper method and keeping the computational expense down from the filter method is proposed.
为了利用过滤方法计算简单和绕封方法精度高的优点，提出一种组合过滤和绕封方法的特征选择新方法。
This paper carries out the study from the variety of different feature selection functions.
本文是从多种特征选择函数的差异性着手进行研究的。
Dimension reduction techniques were discussed from the two aspects: feature selection and dimension transformation.
从属性选择和维变换两个方面对维规约技术进行了概括。
Then the optimum feature subset is selected from the feature genes with Backward Selection Search Method algorithm and independent tests.
通过“两两冗余”后，依据后向搜索算法选定最优特征子集。
The author gains insights from attribute reduction based on discernability matrix and proposes a few rough-set based text feature selection algorithms, i. e. , DB1, DB2 and LDB.
作者从基于分辨矩阵的粗糙集属性约简中受到启发，提出了一系列基于粗集理论的文本特征选择算法，即DB1、DB2、LDB。
Secondly, introduce a feature selection method based on the weight of normal from SVM model.
第二，介绍了一种基于法矢量权重的特征选取方法。
Secondly, introduce a feature selection method based on the weight of normal from SVM model.
第二，介绍了一种基于法矢量权重的特征选取方法。
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