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sparseness

网络  稀少; 稀疏; 稀疏性; 稀疏度; 稀疏化

COCA.37869

英英释义

noun

双语例句

  • Data sparseness led by the lack of training data restricts the promotion of the supervised methods.
    由训练语料不足导致的数据稀疏问题限制了有指导方法的推广。
  • But it also has many problems, such as data sparseness problem, the system scalability problem and time factor problem.
    但协同过滤推荐算法还不够成熟,仍然存在一些问题,如数据稀疏性问题、系统的可扩展性问题和时间因素问题等。
  • In this paper, an improved algorithm of image feature extracting for independent component analysis was proposed based on basic functions'maximization of sparseness.
    利用基函数稀疏性的极大值特点,本文提出一种改进的基于独立分量分析的图像特征提取算法。
  • At the same time a new solution to the sparseness of training text is put forward.
    同时对训练文本的稀疏问题提出了一种新的解决方案。
  • The hierarchical clustering method is applied to deal with the problem that the solution of MLS-SVM is lack of sparseness.
    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。
  • This approach make use of the sparseness of speech signals and the independent characteristics of speech signals. Use the fast quasi-newton method, it greatly simplify the Hessian matrix, and it also greatly increases the speed of operation.
    该方法运用语音信号的稀疏性和语音信号之间相互独立的特性,使用快速相对牛顿法,使得在牛顿法中,求海森阵的步骤大为简化,大大提高了运算速度。
  • The array antenna with low side lobe is designed by employing the Taylor synthesis and density taper sparseness with an overall consideration of the radiation and scattering performance. The side lobe of the array antenna is optimized by random search.
    在综合考虑辐射和散射特性的基础上利用泰勒综合和密度锥削稀疏技术设计了低副瓣电平阵列天线,并采用随机遍历算法对阵列天线副瓣进行了优化。
  • Application of Combined Norm Constrained Sparseness Spike Inverse
    L1-L2范数联合约束稀疏脉冲反演的应用
  • This strategy can reduce the data sparseness and keep the search efficiency meanwhile.
    这种优化策略可以在保证该算法数据查找效率不变的同时,进一步减少数据稀疏,提高空间利用率。
  • Summing up the history and the current development of the personalized recommendation system, and pointing out the two bottleneck problems of "cold start" and "data sparseness" existing in the collaborative filtering recommendation algorithm. 2.
    本文主要做了以下研究工作:1.对个性化推荐系统的发展历史及现状进行了综述,指出了协同过滤推荐算法中存在的冷开始和数据稀疏性两大瓶颈问题。