The 1-Nearest-Neighbor Rule (1-NN) is the simplest and most natural classification rule.
最邻近法则（1-NN）是最简单和最为自然的分类法则。
The misclassification probability of the nearest neighbor decision rule won't exceed 2 times of that of Bayes decision rule when the sample number is very large.
最近邻准则是一种次最优准则，当样本数目很大时，最近邻准则的错误率不会超过贝叶斯错误概率的2倍。
A method to predict the subcellular location of proteins is proposed based on the LZ complexity similarity of symbolic sequences and K nearest neighbor rule.
提出了一个基于符号序列LZ复杂性相似度和K近邻规则的蛋白质亚细胞位点类型预测的方法。
Usually, they are limited to assigning objects to the class model that fits best, e. g. by the nearest neighbor rule.
通常，在这个分类模型中可以达到最好分类(例如采用最邻近法则)的对象不多。
Combining this method with the K-nearest neighbor decision rule, a fixed neighborhood, decision algorithm is developed.
将该方法与K—最近邻判决规则结合，提出了用于判别的固定邻域判决算法。
Classification criteria for the nearest neighbor rule.
分类准则为最近邻规则。
Classification criteria for the nearest neighbor rule.
分类准则为最近邻规则。
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