distance function

distance function的基本解释

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[计] 距离函数

与distance function相关的网络例句 [注：此内容来源于网络，仅供参考]

A generalized Gaussian Laplacian eigenmap algorithm based on geodesic distance is proposed,which incorporates geodesic distance and generalized Gaussian function into the original Laplacian eigenmap algorithm.GGLE algorithm can adjust the similarities between nodes of neighborhood graph,and can preserve the different degrees of local properties by using super-Gaussian function,Gaussian function or sub-Gaussian function.Moreover,GGLE can avoid the deficiency of Euclidean distance by using geodesic distance when neighborhoods of data points are enlarged for preserving more neighborhood relations.Experimental results show that the global low-dimensional coordinates obtained by GGLE have different clustering properties and different degrees of preserving local neighborhood structures when different generalized Gaussian functions are used to measuring the similarities between high-dimensional data points.(3) An ensemble-based discriminant algorithm based on GGLE is proposed.

该算法将测地线距离和广义高斯函数融合到传统的拉普拉斯特征映射算法中，可以调整近邻图结点间的相似度，通过选择超高斯、高斯或者次高斯函数来实现不同程度的近邻局部特性的保持；而且当需要保持更多的近邻关系使得数据点邻域增大时，采用测地线距离可以避免欧氏距离度量不合理的缺陷；实验结果表明在用不同的广义高斯函数度量高维数据点间的相似度时，局部近邻结构保持的程度是不同的，GGLE获得的全局低维坐标也呈现出不同的聚类特性。

Isomap is one of the representative techniques of nonlinear dimensionality reduction.It extends classical multidimensional scaling by considering approximate geodesic distance. However,Isomap is sensitive to noise because the approximate geodesic distance is constructed on the basis of Euclidean distance. In this paper,a kernel-induced distance metric defined in the feature space is introduced instead of the Euclidean distance to evaluate the geodesic distance and construct the corresponding neighborhood graph.

采用核方法在特征空间推导出一类异于欧氏距离的新度量，代替等度规特征映射中的对噪声敏感的欧式距离，用新度量构造测地距离和相应的最小近邻图，提高Isomap算法的抗噪声能力。

Apparently, the results is determined extremely by theoretical algorithm. In 147 zinc-finger-containing genes, 50nt identity sequences were extracted. The results hint that 15 genes may related to block evolution. we analyzed the phylogenetic map of these 15 genes by uncorrected distance, Jukes Cantor distance, Tamura distance, Tajima-Nei distance, Jin-Nei distance or Kimura distance correction, but we found no satisfied result according to the block exchange pattern mentioned before. Introducing Bootstrap test to assistant correction, an comparatively perfect map was drown by Neighbor Joining method.

以147个含锌指基因为模板，我们搜索了其中的50nt一致序列作为板块式进化的核心，获得15个相关基因，应用无校正距离、Jukes Cantor校正距离、Tamura校正距离、Tajima-Nei校正距离、Jin-Nei校正距离和Kimura校正距离等分析了他们之间的进化关系，结果与已知的板块交换模式吻合度较差，引入靴带检验对Jukes Cantor校正距离结果进行统计学校正，并重新用Neighbor Joining方法绘制进化图，结果可靠性大大提高。

与distance function相关的网络解释 [注：此内容来源于网络，仅供参考]

distance function：距离函数

记忆基础推理法中有两个主要的要素,分别为距离函数(distance function)与结合函数(combination function). 距离函数的用意在找出最相似的案例;结合函数则将相似案例的属性结合起来,以供预测之用. 记忆基础推理法的优点是它容许各种型态的数据,

symmetric distance function：对称距离函数

symmetric difference 对称差分 | symmetric distance function 对称距离函数 | symmetric figure 对称图形

Kullback-Leibler distance function：距离函数

Kuder-Richardson公式 Kuder-Richardson formula | Kuiper统计量 Kuiper statistic | Kullback-Leibler距离函数 Kullback-Leibler distance function

distance decision function：距离判决函数

03.447 特征编码 feature coding | 03.448 距离判决函数 distance decision function | 03.449 概率判决函数 probability decision function

distance decision function：距 判决函

03.447 特徵编码feature coding | 03.448 距 判决函 distance decision function | 03.449 概 判决函 probability decision function