1) nearest neightbor weight
近邻权
2) adjacency weights
邻近权重
3) weighted nearest neighbor
加权最近邻
1.
It established different NCs based on bootstrapping technique,and evaluated the classification accuracy of every NC by different sorts of weighted nearest neighbors for mixed attributes,then the NCs with low relative generalization error rates were dynamically selected and majority voting was applied to those NCs in order to conduct the final classification results of the ensemble.
基于bootstrapp ing构建不同的个体神经网络,针对混合属性,通过不同的加权最近邻设计评估单个网络的分类精度,在此基础上动态选择误差率较小的神经网络,经过投票形成集成分类结果。
4) Nearest neighbor weight function
近邻权函数
5) weighted knearest neighborhood
加权K近邻
1.
In this paper, based on the characteristics of data set, we put forward weighted knearest neighborhood method.
本文根据数据的特点,改进了传统的K近邻方法,提出了加权K近邻的方法,进一步增强了利用测井数据识别岩性的能力,并在实际应用中证明了本方法的正确性和实用性。
6) k-nearest neighbour weights
k-最邻近权
补充资料：因侵害姓名权、肖像权、名誉权、荣誉权产生的索赔权
因侵害姓名权、肖像权、名誉权、荣誉权产生的索赔权：公民、法人的姓名权、名称权，名誉权、荣誉权、受到侵害的有权要求停止侵害，恢复名誉，消除影响，赔礼道歉，并可以要求赔偿损失。
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