feature selection

To discern positive and negative example fully, feature subset selection plays a great role in learning from examples.

特征选择是示例学习的关键，直接关系到获取的概念的优劣。

Research on a noise-tolerant algorithm for feature subset selection

容忍噪音的特征子集选择算法研究

The heuristic algorithm of minimal feature subset selection is proved very efficient by theoretical analysis and experiment.

通过理论分析和实验，证明了该特征选取方法的高效性。

Optimal Feature Subset Selection for Multi-class Issue

多类问题中最优特征子集选取的研究

Feature selection tended to favor necessary improvements over desirable improvements.

他们的选择必须的特性,而不是可拥有的特性.

And a no-search combined feature selection method based on KNN is proposed.

在此基础上,该文提出了一种基于KNN算法的组合式非搜索特征选择算法.

This thesis designs 4 main algorithms on the base of Feature Selection algorithm Framework.

在特征选择算法框架的基础上,论文先后设计了4种主要的特征选择方法.

Feature selection is the key part in image matching based on features.

提出了基于岩石颗粒特征的图象配准算法.

Feature Subset Selection for KDD

KDD中的特征选择

Feature selection from the keyboard or'teach and learn'from the hand-box .

通过键盘或手动操作盘上的 ‘ teachandlearn ’键来选择测量元素.

PRN combined feature selection algorithm is proposed.

并提出改进的PRN组合 特征选择算法.