1) Bhattacharyya Distance
巴氏距离
1.
Feature Selection Based on the Composition of Bhattacharyya Distance and K-L Decomposition;
巴氏距离和K-L变换结合的特征选择
2.
Scene change detection in video stream based on Bhattacharyya distance
基于巴氏距离的视频流场景变化检测(英文)
3.
By maximizing the feature vector distance between multi-modal clusters in a hyper-sphere space,a Bhattacharyya distance based metrics has been utilized to quantify the scene change.
通过计算高维空间中多模式聚集的最大特征向量距离,提出了基于巴氏距离的场景变化度量,并有效采用低秩Cholesky分解计算近似巴氏距离。
2) Mahalanobis Distance
马氏距离
1.
Computer Program Based on the Mahalanobis Distance for Image Division of Lichee Pictures;
基于马氏距离的荔枝图像分割设计方法
2.
Mahalanobis distance algorithm to separate objective picture based on color characteristic and color clustering;
基于颜色特征和聚类的马氏距离图像分割法
3.
Missing value estimation for gene expression data based on Mahalanobis distance;
基于马氏距离的缺失值填充算法
3) Euclidean distance
欧氏距离
1.
Study on the Quality Control of Flos Chrysanthemi Indici by HPLC Chromatographic Fingerprint with Euclidean Distance;
液相指纹图谱结合欧氏距离对野菊花质量控制的研究
2.
Application study of image Euclidean distance in face recognition;
图像欧氏距离在人脸识别中的应用研究
3.
Line detection based on Euclidean distance;
基于欧氏距离的实时直线检测算法
4) Euclid distance
欧氏距离
1.
Firstly, reconstruct attractors in phase spaces using chaotic theory,Secondly fit the attractor s evolvement using BP neural networks, because selecting neural network s input training data using Euclid distance and correlation, improve neural network s associative memory and ratiocinative ability, can better fit the attractor s evolvement.
提出一种将混沌理论、关联度和神经网络相结合的短期负荷预测模型,首先利用混沌理论重构负荷时间序列的相空间吸引子,然后用BP神经网络来拟合空间吸引子的演化,由于使用空间欧氏距离和关联度联合来选取神经网络的训练样本,这样就提高了神经网络对负荷序列混沌特性的联想和泛化推理能力,能够更好的拟合吸引子的演化。
2.
Secondly,the attractor s evolvement using BP neural networks is made,and the neural network s input data using Euclid distance is selected.
提出一种将混沌时间序列和神经网络相结合的短期负荷预测方法,利用混沌理论重构相空间的吸引子,然后用BP神经网络来拟合空间吸引子的演化,同时利用空间欧氏距离来选取神经网络的输入样本,实例预测结果表明所提出方法的有效性和可行性。
3.
Because the values of Euclid distance limits used in the forecasting evidently impact the fore.
在混沌理论中,采用不同的嵌入维数计算方法所获得的维数略有不同,而在不同嵌入维数下对同一负荷时间序列进行预测的结果也不同,对此,文章提出了多嵌入维数的负荷预测方法,将不同维数下的预测结果进行加权平均;在预测过程中欧氏距离极限的取值对预测结果有很大影响,文中采用动态调整法进行选取以使预测误差最小。
5) Portland's distance
兰氏距离
6) Haosh distance formula
郝氏距离
补充资料：巴-巴氏病