1) nearest neighbor searching
近邻搜索
1.
This predictor takes the K nearest neighbor searching method to gain the K neighbors of the sample to be predicted,trains a RBF network with these neighbors,and predicts the sample with well trained RBF network.
该模型采用K最近邻搜索方法得到待预测样本的K个近邻,用近邻样本进行RBF神经网络建模,用训练好的RBF神经网络对待预测样本进行预测。
2) neighbor finding
邻近搜索
1.
The algorithm of neighbor finding from back to front with decomposing Quaternary code is presented.
分析了球面Quaternary编码的特点,给出了三角格网方向判断的规则,并对该算法与Bartholdi邻近搜索算法的时间复杂度进行了对比。
2.
A neighbor finding algorithm is presented in details,and the relationships between diamonds and hierarchy,diamond and grid,and grids and hierarchy are constructed using their adjacent .
用线性四叉树成熟的Morton编码作为关键字来标识菱形块,发展了具有固定方向(fixed orientation)的“块”层次编码技术及邻近搜索算法。
3) nearest neighbor search
最近邻搜索
1.
This paper absorbs ideas of previous researches and proposes a novel nearest neighbor search algorithm based on dynamic threshold.
作者在前人工作成果的基础上,提出并实现了一种基于最优投影和动态阈值调整的最近邻搜索算法DTA(Dynamic Threshold Algorithm);证明了最优投影线定理和投影邻域定理;并分析了DTA算法与SNN算法相比在算法性能上的优势。
4) neighbor point searching
邻近点搜索
5) near neighbor search
最邻近搜索
6) K-NN search
K-近邻搜索
补充资料：近邻
挨得较近的邻居：远亲不如～。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条