以往这类问题主要针对一维稀疏向量，且都直接采用正弦信号模型开始分析。
Previous sparse band coherent processing methods are mainly for one-dimensional sparse vector and directly use sine signal model for analysis.
稀疏向量法通过利用向量的稀疏性来提高求解矩阵方程的效率，它被成功地应用到电力系统分析的众多问题。
The sparse vector method enhances the efficiency of matrix solution algorithms by exploiting the vector sparsity. It has been successfully applied to many problems arising in power systems.
作为一个结果，每个文档表示相对于所有这些特征(所有文件有相同数量的功能)，大部分的值将为零，一个非常稀疏向量生成。
As a result, each document is represented with respect to all such features (all documents have the same number of features) but most of the values will be zero, creating a very sparse vector.
Meschach可以解稠密或稀疏线性方程组、计算特征值和特征向量和解最小平方问题，另外还有其它功能。
Meschach was designed to solve systems of dense or sparse linear equations, compute eigenvalues and eigenvectors, and solve least squares problems, among other things.
考虑在工作站机群上实现大型稀疏矩阵和向量乘的负载平衡。
The load-balanced multiplication of a large sparse matrix with vector on workstation cluster is considered.
它拥有众多的优良特性，如利用核技术避免了解的局部最小、具有解的稀疏性、通过界限的作用达到容量控制或支持向量数目的控制等等。
It has many advantages, such as using kernel function to avoid local minimal point, sparse nature of solutions, limit used to control capacity or the number of support vectors, etc.
编程中采用了稀疏矩阵向量相乘的优化技术。
Optimization techniques for the sparse matrix vector multiplication are adopted in programming.
运用谱系聚类方法解决多核最小二乘支持向量机的解缺乏稀疏性的问题。
The hierarchical clustering method is applied to deal with the problem that the solution of MLS-SVM is lack of sparseness.
但是，由于顾客向量的平均值很稀疏，算法的执行更倾向于接近o (M +N)。
However, because the average customer vector is extremely sparse, the algorithm's performance tends to be closer to o (m + n).
为避免在进行核融合时，支持向量机稀疏性的缺失，提出将数据映射到稀疏特征空间进行研究。
In research, sample data were mapped to sparse feature space to prevent the loss of SVM's sparsity when the kernels were fused.
文本分类中特征向量空间是高维和稀疏的，降维处理是分类的关键步骤。
Feature space is high dimensional and sparse in text categorization, the process of dimension reduction is a very key problem for large-scale text categorization.
支持向量回归机是一种解决回归问题的重要方法，其预测速度与支持向量的稀疏性成正比。
Support Vector regression is an important kind of method for regression problems. The predicting speed of Support Vector regression is proportional to its sparseness.
最小二乘支持向量机相比传统的支持向量机，丧失了解的稀疏性，影响了二次学习的效率。
Compared with the classical Support Vector Machines, the Least Squares Support Vector Machines lose the sparseness, which would influence the efficiency of re-learning.
对原有的最小二乘支持向量机在稀疏性上进行了改进，并通过实验，对改进后的最小二乘支持向量机的分类效果进行了验证。
To conclude a sparse solution, we present an improved algorithm for Least Squares Support Vector Machines, and prove its effect by an experiment.
提出重构样本库的概念及构建算法，获得稀疏样本库，减少特征向量维数。
To get sparse sample library and reduce eigenvector dimension, a new concept of reconstructing sample library and its corresponding algorithm are introduced and presented, respectively.
支持向量回归机是一种解决回归问题的重要方法，其预测速度与支持向量的稀疏性成正比。
The experiments on several pattern classification problems show that HS-LSSVM has high sparseness while holds good classification performance and its sparse process is fast.
该方法首先通过在加权最小二乘 支持向量机的基础上加入对数据偏斜的处理，解决了元 信息分类时关键词特征稀疏和样本高度不均衡问题；
Since the feature of the meta-information classification keywords is sparse and the distributing of sample is unbalanced, this thesis considered the factor of data skew based on LS-VSM.
文章首先将稀疏分解法等同于支撑向量回归(SVR)的一种形式，为稀疏分解法提供新的直观解释和求解方法。
First, algorithm equate the sparse_decomposition to a form of support_vector regression (SVR), for providing new interpretation and solution.
文章首先将稀疏分解法等同于支撑向量回归(SVR)的一种形式，为稀疏分解法提供新的直观解释和求解方法。
First, algorithm equate the sparse_decomposition to a form of support_vector regression (SVR), for providing new interpretation and solution.
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