1) k-NN estimator
k-近邻估计
2) kNN density estimation
k近邻密度估计
3) K-Nearest Neighbors Kernel Estimation
K-近邻核估计
1.
In this paper K-Nearest Neighbors Kernel Estimation method was applied to forecasting the throughput of empty containers at Hong Kong Port based on regression analysis,which was compared with parametric regression.
以香港港口为例,采用K-近邻核估计对港口空箱吞吐量进行回归计算,将计算结果与参数回归方法的计算结果进行比较,表明K-近邻核估计的拟合效果和预测精度都优于多元线性回归方法的拟合效果和预测精度。
4) generalized k-nearest neighbor
广义k-最近邻估计
1.
The non-parametric density estimation—generalized k-nearest neighbor(GKNN) estimation based novel independent component analysis(ICA) algorithm which is fully blind to the sources is proposed using a linear ICA neural network.
基于概率密度非参数估计的广义k-最近邻估计(GKNN)和线性独立成分分析(ICA)神经网络,提出了一种新的ICA非参数算法,实现了对源信号分布的全"盲"要求。
5) nearest neighbor estimator
近邻估计
1.
Least square nearest neighbor estimator of non-linear semiparametric models;
非线性半参数模型最小二乘近邻估计
6) neighbor estimation
邻近估计
补充资料：Nearestneighbor(近邻取样)
nearest neighbor (近邻取样)
又被称为point sampling(点取样)，是一种较简单材质影像插补的处理方式。会使用包含像素最多部分的图素来贴图。换句话说就是哪一个图素占到最多的像素，就用那个图素来贴图。这种处理方式因为速度比较快，常被用于早期3d游戏开发，不过材质的品质较差。
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