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运算复杂性
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  computational complexity
    In order to reduce the computational complexity of SVC (Support Vector Clusterin g) on the basis of the proximity graph model developed by Yang et al. , the Eucli dean distance in the Hilbert space is calculated by using a Mercer kernel, which is used as the weight criterion to generate a MST (Minimum Spanning Tree). The connectivity estimations are then lowered by only checking the linkages between the edges that construct the main stem of the MST.
    为了降低支持向量聚类 (SupportVectorClustering ,SVC)的运算复杂性 ,基于Yang等提出的邻近图法 ,用Mercer核来表达Hilbert空间中的Euclidean距离 ,以此作为边的权重度量来生成最小生成树 (MinimumSpanningTree ,MST) ,并只对MST的主干进行SVC连接运算 .
短句来源
    This approach applied a processing strategy that from coarse to fine realized region compactibility and overcome the computational complexity of single Gibbs Random Field label structure, decrease computational time, and acquired a better segmentation result.
    算法应用由粗至细的处理策略,实现了图像的区域紧凑性,克服了一层GRF标记场结构的运算复杂性,减少了运算时间,并且能够获得较好的分割结果.
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  “运算复杂性”译为未确定词的双语例句
    The computation complexity and data abstract probability were used to consider the distributed schema of spatial relation computation, and a respective schema and spatial analysis web based transportation protocol were proposed and implemented in WebGIS.
    采用数据抽取率与运算复杂性两个定性指标来考虑空间关系运算的分布式方案,设计了空间对象关系运算的分布式方案和相应的空间分析Web传输协议。 并在WebGIS中得到了实现
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  computational complexity
Like the lattice factorization, the decomposition presented here asymptotically reduces the computational complexity of the transform by a factor two.
      
Computational complexity of (2,2) path chromatic number problem
      
The algorithm exhibits either a solution or its nonexistence after at most, steps (where n is the dimension of the problem) and the computational complexity is at most1/3n3 + O(n2)
      
This paper studies the computational complexity of this problem for graphs with small diameters.
      
However, its computational complexity was comparable to that of the normalized least-mean-square (NLMS) algorithm.
      
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Based on the characters of spatial relation computation of Network Oriented WebGIS, the authors made a systematic study on spatial relation computation to meet the demand of distributed spatial analysis. The computation complexity and data abstract probability were used to consider the distributed schema of spatial relation computation, and a respective schema and spatial analysis web based transportation protocol were proposed and implemented in WebGIS.

针对当前面向网络WebGIS的特点,对空间关系运算进行了系统的研究以提供分布式空间分析的需要。采用数据抽取率与运算复杂性两个定性指标来考虑空间关系运算的分布式方案,设计了空间对象关系运算的分布式方案和相应的空间分析Web传输协议。并在WebGIS中得到了实现

Data mining is searching for stable, meaningful, and expressible knowledge patterns in a huge data bank. The authors state the main tasks and classification of DM and the intelligent computation methods in DM, and propose the concept of information granule. The intruduction of information granule can make the implied sence of the complex data understood easily, can make them modularized, and can reduce the complexity of treating them. So the data information is synthesed and compressed. The construction and...

Data mining is searching for stable, meaningful, and expressible knowledge patterns in a huge data bank. The authors state the main tasks and classification of DM and the intelligent computation methods in DM, and propose the concept of information granule. The intruduction of information granule can make the implied sence of the complex data understood easily, can make them modularized, and can reduce the complexity of treating them. So the data information is synthesed and compressed. The construction and management of the information granules are based on fuzzy theory, and the quantitative expression of them is done by means of fuzzy sets. Granularity can be diversified in terms of the interest degree of a problem in order that the information granules can cover over all the studied data. Finally, the various relationship among the granules and the further effects of the information granules in DM are discussed.

为在海量的数据仓库中搜寻稳定的、有意义的且易于表示的知识模式 ,简要介绍了数据挖掘的任务、分类及智能计算方法的作用 ,提出了信息颗粒的概念 .信息颗粒的引入 ,使人们更容易地理解各种复杂数据所隐含的意义 ,对数据进行模块化处理 ,从而降低运算的复杂性 ,起到信息综合、信息压缩的作用 .另外 ,根据模糊性理论构造和管理信息颗粒 ,用模糊集对信息颗粒进行量化表示 ,计算信息颗粒度 ,针对具体问题根据兴趣度灵活而有效地选择信息粒度 ,使它们有效地覆盖所研究的整个数据领域 .最后 ,讨论了颗粒间的各种关系 ,并给出了信息颗粒在数据挖掘中的进一步作用 .

In order to reduce the computational complexity of SVC (Support Vector Clusterin g) on the basis of the proximity graph model developed by Yang et al., the Eucli dean distance in the Hilbert space is calculated by using a Mercer kernel, which is used as the weight criterion to generate a MST (Minimum Spanning Tree). The connectivity estimations are then lowered by only checking the linkages between the edges that construct the main stem of the MST. Moreover, the non-compatibil ity degree is defined to support...

In order to reduce the computational complexity of SVC (Support Vector Clusterin g) on the basis of the proximity graph model developed by Yang et al., the Eucli dean distance in the Hilbert space is calculated by using a Mercer kernel, which is used as the weight criterion to generate a MST (Minimum Spanning Tree). The connectivity estimations are then lowered by only checking the linkages between the edges that construct the main stem of the MST. Moreover, the non-compatibil ity degree is defined to support the edge selection during linkage estimations. Experimental results confirm that, compared with the proximity graph model, the proposed approach is of faster speed, optimized clustering quality and strong ab ility of noise suppression, which makes SVC advantageous in dealing with large d ata sets.

为了降低支持向量聚类 (SupportVectorClustering ,SVC)的运算复杂性 ,基于Yang等提出的邻近图法 ,用Mercer核来表达Hilbert空间中的Euclidean距离 ,以此作为边的权重度量来生成最小生成树 (MinimumSpanningTree ,MST) ,并只对MST的主干进行SVC连接运算 .文中还定义了不相容性度量 ,并将其作为SVC连接运算中边的选择依据 .试验证明 ,改进后算法的运行速度及聚类效果均优于邻近图法 ,特别是对大数据集的处理具有明显的优势 ,且具有一定的抗噪能力 .

 
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