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。
应用推荐