盒维数的简单统计结果可以作为PQ神经网络分类器的输入特征量。
After simple statistics, the dimension can act as the input vector of ANN for PQ classification.
随着数据集的数据量和维数的增加，建立高效的、适用于大型数据集的分类法已成为数据挖掘的一个挑战性问题。
With the growth of data in volume and dimensionality, it has become a very challenging problem to build a high-efficient classifier for large databases.
用高斯-牛顿误差最小法将六维观测量转化为四元数，作为观测量的一部分，显著减少了直接使用EKF的计算量。
Gauss-Newton error minimization is used to transform six-dimentional reference vector to quaternion as a part of observations for EKF, which significantly reduces the computational requirement.
一个支持向量机的支持向量数相关的VC维是怎样的？有一个公式，关于这两个量？
How is the VC dimension of a support-vector machine related to its number of support vectors? Is there a formula relating these two quantities?
数据的主成分提取是为了降低数据集的维数，减少计算量。
Extracting the primary components of data is to decrease the dimension of the data in order to decrease the quantity of calculation.
通过用虚假临界点法计算嵌入维数可以使小数据量法更加完善。
The small-data method is improved by false nearest neighbor method calculating embedding dimension.
土壤的质地类型、含盐量、有机质含量等是决定土壤粒径分布分形维数的主要因子。
Texture type, salt content, organic matter content, etc. are the major factors determining the fractal dimension of soil particle-size distribution.
该方法可以有效地降低特征维数，降低了计算量，并且能够增大不同类别的特征间的距离，有助于实现更好的分类结果。
This method can reduce dimension of the feature and calculated amount, and besides it can increase the distance between the feature, this all contribute to a better classification result.
当数据维数很高时，传统聚类算法也面临挑战：随着维数的增加，计算量迅速增大；
The traditional clustering algorithm is facing challenges, when the dimensional of data clustering is high:with the dimension increased, the calculated quantity rapidly does;
但是当系统模型的维数很高时，延迟很大时，这种方法会导致计算量的增加。
But these methods lead to increasing of calculation when the dimension or time delay in the system is very great.
该算法和传统色彩量化算法相比，降低了特征向量的维数，计算量小，受光照强度影响较小，提高了检索的性能。
Then we train SVM classifier using visual features such as color, texture, and shape. Experimental results show that this approach can improve precision and recall effectively.
电力系统的节点导纳方程组一般是维数很大的线性方程组。为了减少导纳矩阵的运算量，节省计算机内存，人们提出了很多优化编号方法。
Many optimal bus-labeling methods have been proposed in order to improve the efficiency of computation of nodal admittance matrix in power systems and to save the computer memories.
但用G P算法求关联维数存在抗干扰能力较差、可靠性不稳定、运算量巨大等缺点。先对相空间进行奇异谱分析，进而对原始相空间进行旋转，使其成为正交的等效空间，然后再使用G P算法。
But G-P algorithm which be used to calculate D2 have some shortages, such as being robust against noise, not stable, and needing a big operation.
但用G P算法求关联维数存在抗干扰能力较差、可靠性不稳定、运算量巨大等缺点。先对相空间进行奇异谱分析，进而对原始相空间进行旋转，使其成为正交的等效空间，然后再使用G P算法。
But G-P algorithm which be used to calculate D2 have some shortages, such as being robust against noise, not stable, and needing a big operation.
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