研究了模式识别中的特征选择方法，采用最近邻分类正确率作为特征选择的性能评价函数。
The paper studies the methods of feature selection in pattern recognition, and USES nearest neighbor classification accuracy as the evaluation criteria for feature selection.
为了验证特征的有效性，使用最近邻及概率神经网络分类器进行了目标识别，得到满意的识别率。
In order to validate character validity, use NearestNeighbor (NN) and probabilistic neural network (PNN) classification identify target, gain content identification probability.
该方法首先利用核主元分析对人脸图像进行特征提取，然后依据支持向量机与最近邻准则对所提取的核主元特征进行分类识别。
Firstly KPCA is used to extract the features of human face image, and then SVM combined with the nearest distance rule is used for classification, which depends on the kernel principal components.
在对LF M信号实现分选的基础上，选取待识信号的瞬时频率作为识别特征，利用最近邻方法对信号进行识别。
Supposing the LFM signals had been sorted and selecting the instantaneous frequency as identification character, the closest method is used to distinguish the signals.
用最近邻法进行分类和识别。
用最近邻法进行分类和识别。
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