K-NN is a method of classifying based on statistics.
近邻是基于统计的分类方法。
A partial features based similarity K-NN retrieval method is proposed.
提出了基于部分特征的K -近邻相似检索方法。
Compared with K-NN and ENN identity verification systems, the performance of verification system using improved ENN is enhanced.
实验表明，改进ENN的身份鉴别融合系统的认证率比K-NN和传统ENN融合系统有所提高。
The Euclidean distance is usually chosen as the similarity measure in the conventional K-NN algorithm, which usually relates to all attributes.
传统的K-近邻算法选择的相似性度量通常是欧几里德距离的倒数，这种距离通常涉及所有的特征。
According to the characteristics of injection products, a K-Nearest Neighbors (K-NN) case retrieval strategy was proposed based on rough set and simulated annealing algorithm.
针对注塑产品特点，提出了基于粗糙集和模拟退火算法的事例最邻近检索策略。
At the same time, we used relevance feedback and machine learning used in image retrieval. K-NN, BP neural network and support vector machine classifiers were used in experiments.
同时本文将机器学习和相关反馈结合起来用于图像检索，在实验中使用了K - NN、BP神经网络和支持向量机分类器。
By revising a K-NN classification method based on evidence theory, a new K-NN classification method based on evidence reasoning model is got, which made the classification result more accurate.
本文对基于证据理论的k - NN分类方法进行了修正，得到了基于证据推理模型的k - NN分类方法，使分类结果更加精确。
By revising a K-NN classification method based on evidence theory, a new K-NN classification method based on evidence reasoning model is got, which made the classification result more accurate.
本文对基于证据理论的k - NN分类方法进行了修正，得到了基于证据推理模型的k - NN分类方法，使分类结果更加精确。
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