We bring out our video retrieval method based on multi-feature data association histogram and C-Mean fuzzy clustering algorithm.
基于多特征联合分布直方图理论和模糊c -均值聚类算法，我们提出了新的视频流模糊检索方法。
Multi-streaming is an important feature of SCTP, especially when you consider some of the control and data issues in protocol design.
多流处理是SCTP的一个重要特性，尤其是在协议的设计中考虑一些控制和数据的问题时更是如此。
The feature also lets you perform multi-system workload balancing, which can be used to conserve data center costs.
这个特性还允许执行多系统负载平衡，可以用来节约数据中心的成本。
Because it's massive and unstructured, Feature-Oriented Object Level (FOOL) and Multi-servers cluster system are used to integrate and manage spatial data effectively.
由于其海量和非结构化等特征，实体对象层次模型、群集多服务器体系被提出用以集成并实施高效的空间数据管理。
According to the feature of mass discrete data, an efficient retrieval method is stated by referring to the philosophy of distributive database, parallel computing, grid computing and multi-threading.
本文针对海量离散数据的特点，借鉴分布式数据库、并行运算、网格计算及多线程的思想，提出了一套海量离散数据快速检索方法。
This paper reports a multi-feature stored program controlled (SPC) switching system which has voice and data exchange feature and multi-interface.
本文介绍一种具有话音和数据交换功能且具有多种用户接口的多功能交换系统。
The data fusion level module mainly handles the metrical data of multi-sensors and extracts the feature of faults.
数据级融合模块主要对多传感器的测量信号进行处理，提取出故障诊断的特征信息。
Since the data samples in machine learning and pattern recognition problems generally distribute in multi-modal distribution, this thesis proposed a prototype based feature ranking model.
由于模式识别、机器学习等问题的复杂性比较高，数据分布通常呈现多模态分布。
For data fusion algorithms of multi-sensors, the D-S rule is widely used extensively. But it can not resolve the problem of feature collision ideally.
在多传感器特征信息融合算法中，D - S理论得到了广泛应用，但该理论在处理多特征冲突问题时识别效果不十分理想。
We study the algorithms of feature-level fusion based on the viewpoint of dependence and independence multi-variant data analysis, respectively.
首先从多元数据分析的角度，研究了基于相关性的多元数据分析和基于独立性的多元数据分析的特征融合方法。
Multi-feature cubes(MF-cubes) compute complex data mining queries involving multiple dependent aggregates at multiple granularities.
多特征方用于计算复杂数据挖掘查询，具有多粒度上多个依赖聚集计算的典型特点。
Multi-feature cubes(MF-cubes) compute complex data mining queries involving multiple dependent aggregates at multiple granularities.
多特征方用于计算复杂数据挖掘查询，具有多粒度上多个依赖聚集计算的典型特点。
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