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sliding window model
相关语句
  滑动窗口模型
     According to the features in data stream processing, we further lucubrated the functions and characters of sliding window model in data stream processing, based on which, the ideas of hierarchy and multiple time granularities is introduced into the sliding window models, and put forward an approach to modeling hierarchical windows model.
     深入研究了滑动窗口模型在数据流处理中的作用和特点,并根据数据流应用中的特征,将层次划分的思想和多粒度描述引入到滑动窗口模型中来,提出了一种层次窗口模型的建模方法,对非平均的窗口划分方法展开了探讨。
短句来源
  滑窗式模型
     Research of Ultrasonic Barrier Flow Volume Measure Based on the Algorithm of Sliding Window Model Prediction
     基于滑窗式模型预估的超声波堰式流量测试研究
短句来源
  相似匹配句对
     The Window
     窗(英文)
短句来源
     The Window
     窗口(英文)
短句来源
     Join algorithms of compound sliding window
     复合滑动窗口连接算法
短句来源
     The security of sliding window data transfer
     滑动窗口数据传输的安全问题
短句来源
     A mathematical model for sliding window polynomial fitting is proposed.
     提出了滑动窗多项式拟合数学模型。
短句来源
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  sliding window model
The scheme extends to the sliding window model with a log (ε n) factor increase in space, where n is the size of the sliding window.
      
With the sliding window model, we define the support of a temporal pattern as follows.
      
The sliding-window model is a variation of the streaming model in which only more recent data elements in the stream are considered.
      
The most recent data is aggregated following the sliding window model.
      
To address this point, in the sliding window model employed, only the occurrences of singleton items are being counted in the first time window.
      
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In accordance with the research of ultrasonic barrier flow volume measure. the conception of sliding window is applied to the signal measured. The algorithm of model prediction with RBF neural network is described. The fault caused by the fluctuation of the liquid surface is diagnosed and recovered. The precision of barrier flow volume measurement is improved. The algorithm of sliding window model prediction will be helpful to the research of other similar measurements.

结合超声波堰式流量测试方法的研究 ,在信号处理领域首次引进了“滑动窗口”的概念 ,提出了一种滑窗式模型预估的算法 ,给出了RBF神经网络实现模型实时预估的方法 ,解决了液面不平引起流量测试故障实时恢复的问题 ,提高了堰式流量测试的精度。滑窗式模型预估的算法对其他物理量的测试同样有一定的指导作用。

Studied in this paper is the problem of identifying correlations between two multiple-dimensions data streams under constrained resources. A novel online canonical correlation analysis (CCA) algorithm based on approximate technique,called QuickCCA,is proposed. To solve bottleneck of CCA's performance,QuickCCA uses a column-sampling with non-equal probability to compress the numbers of tuples and construct synopsis matrix first. And based on the synopsis matrix,the most k principal correlation coefficients between...

Studied in this paper is the problem of identifying correlations between two multiple-dimensions data streams under constrained resources. A novel online canonical correlation analysis (CCA) algorithm based on approximate technique,called QuickCCA,is proposed. To solve bottleneck of CCA's performance,QuickCCA uses a column-sampling with non-equal probability to compress the numbers of tuples and construct synopsis matrix first. And based on the synopsis matrix,the most k principal correlation coefficients between evolving multiple-dimensions data streams are computed rapidly. Theoretic analysis and experiments indicate that QuickCCA can accurately identify correlations between two multiple-dimensions data streams in synchronic sliding windows model. Compared with the existing correlation analysis algorithm for data streams,the QuickCCA algorithm reduces complexity of computation efficiently and trades accuracy with performance. It can be presented as a generic tool for a multitude of applications on data stream mining problems.

为了解决在资源受限的计算环境下快速检测高维数据流之间相关性的问题,提出一种新颖的在线典型相关性分析(CCA)算法QuickCCA,针对传统CCA计算中的性能瓶颈,首先采用不等概列采样技术约减流元组的数量,形成概要矩阵;然后在概要矩阵的基础上增量地计算多维数据流之间的前k个典型相关系数.经理论分析和实验证明,QuickCCA能够在线精确地识别同步滑动窗口模式下多维数据流之间的相关性.与已有分析多数据流相关性的算法相比,QuickCCA显著地降低了计算复杂度,并且能够在精度和性能之间折中,可以作为通用的分析工具广泛应用于数据流挖掘领域.

 
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