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    Design of Large Data Acquisition System
    (2) The HMMs’ parameters are further trained by the method of iterative learning from a large data set;
    The computation results show that parallel genetic algorithm is fit for data reduction problem with large scale and large data sample point.
    A Novel Classification Method in Large Data
    The Large Data Direct Classifying Method Based on Hyper Surface
    This system applies Active Server Pages (ASP) technique and other related Web techniques to carry out composite data query based on the database with large data size and complicated data structure.
    Aiming at the needs of large data, high transport and complex operation, a real- time video- based processing system using SAA7115 as its capture A/D, TMS320DM642 as its core,SAA7105 as its display D/A ,is designed, and FPGA is used to control the display to achieve advanced function.
    The system has the characteristics of high automation degree,large data collection capacity and strong system reliability,etc.
    With Bisecting K-means algorithm,experiments show that the hierarchy of ontology concept is more refined and has lower time-complexity. Bisecting K-means algorithm is especially suited for handling large data sets.
    实验结果表明,Bisecting K-means算法产生的本体概念的层次更加精炼,时间复杂度较小,特别适合用于处理大型数据集.
    Parameter learning for Bayesian networks with large data set
    A Feature Selection Method Fitting for Large Data Set
    Extracting deeply knowledge, optimal operation condition and manageable pattern from large data of production and management, namely, process industrial data mining, is one of the most important technologies to realize online monitoring, fault diagnosis, safety estimation, product management, marketing analysis and prediction and so on of process industry. In addition, it can provide more effective decision support for industrial safety operation and efficient manufacture.
    To the great of large data problem of robotic graphics, a new technique of partition coding and transportation for the robot object is presented, which using projection method and kinematical principle to implement the quickly partition and coding of the robot object.
    It is a new effective way that takingthe advantage of database technology to improve or design new efficientalgorithms suitable for large data sets, which is being explored by manyscholars.
    1. Based on the analysis of the time cost and space cost as well asthe necessity of using discernibility matrix to obtain core attributes inattribute reduction algorithm, propose an improved attribute reductionalgorithm based on Rough set and database technology. Experimentsshow that its efficiency on large data sets is much faster than some otherattribute reduction algorithms based on memory and is easy to realize anduse.
    On the other hand,,RST has the features of handling and reducing large data sets while has lower classification accuracy than SVMs.
    This system applies Active Server Pages (ASP) technique and other related Web techniques to carry out composite data query based on the database with large data size and complicated data structure. Such techniques as data analysis, control and processing are integrated with the information query and distribution system using Web technique, taking the consideration of application's security and the system's performance. Design of this solution is implemented according to users' need directly.
    With the expansive applications and exponent growth, people are eager to get newly tools and technologies more and more to extract interest information from large data source intelligently. KDD/DM is a Intelligent Data Analysis(IDA) technology proposed for that intention.
    数据库的广泛应用和数据量的飞速增长,使人们迫切地感到需要新的技术和工具以支持从大量的数据中智能地、自动地抽取出有价值的知识或信息,数据库知识发现就是为解决上述问题而提出的智能数据分析(Intelligent Data Analysis,IDA)技术。
    However, the large data and information load contained by image signal put much difficulty in real-time image processing, though they are definitely helpful to realize machine intelligence in the future.
    5.T0 the disposal of super large data set, the building of cluster systems is put forward and the algorithms of task disassemble and computation are designed.


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