数据采掘的英文

• data excavating
• data mining

• "数据"英文翻译    data; record; information
• "采掘"英文翻译    digging; hewing; cutting; ex ...
• "数据开发，数据采集，数据采掘，" 英文翻译 :    data mining
• "数据采集" 英文翻译 :    acquisition, data; da data acquisition; dacq; data acquistion; data aquisition; data capture; data collection; data gathering; data logging
• "数据采集板" 英文翻译 :    data acquisition board
• "数据采集架" 英文翻译 :    dac data acquisition chassis
• "数据采集卡" 英文翻译 :    data acquisition board; pcl-725 pcl-818l
• "数据采集器" 英文翻译 :    data acquisition unit; data acquisitor; data trap; datac
• "数据采集网" 英文翻译 :    data acquisition network
• "数据采集站" 英文翻译 :    das data acquisition station; data collection station
• "数据采样" 英文翻译 :    sampling
• "数据采样率" 英文翻译 :    data sampling rate
• "元数据采集" 英文翻译 :    metadata acquisition
• "并行数据采集" 英文翻译 :    parallel data acquisition
• "串行数据采集" 英文翻译 :    serial data acquisition
• "地理数据采编" 英文翻译 :    geographical data handling
• "地面数据采集" 英文翻译 :    surface data acquisition
• "多路数据采集" 英文翻译 :    multi-channel data acquisition
• "分散数据采集" 英文翻译 :    decentralized data acquisition
• "高速数据采集" 英文翻译 :    high-speed data acquisition; hsda high speed data acquisition
• "管理数据采集" 英文翻译 :    management data collection
• "海上数据采集" 英文翻译 :    offshore acquisition
• "联机数据采集" 英文翻译 :    on-line data acquisition
• "三维数据采集" 英文翻译 :    3-d data collection; three dimensional data collection
• "数据采集板卡" 英文翻译 :    data-acquisition boards

• With the boom of date mining , bayesian network has been paid great attention and again in the limeligh < because of the naturai relation between the mathematical statistics and data mining
  由于概率统计与数据采掘的天然联系。数学采据兴起后贝叶斯网络日益受到重视，再次成为引人注目的热点
• Therefore , it is the same with data mining with probability statistic character and knowledge discovery problems , especially with die problems that obtain sample information or need high cost
  因此，适用于具有概率统计特征的数据采掘和知识发现问题，尤其是样本难以获取或代价过于昂贵的问题。
• Comparing with non - bnyain methods , it ' s prominent featares lay in that it combines the prior and posterior information , which avoids the disadvantag of subjective bias caused by simply using the prior information only , of blind search caused by the incomplete sample information , of noise affection caused by simply using the sample information only if we choice a suitable priof , we can conduct the bayesian leaming effectively , so it fits the problems of data mining and machine leaming that possess charaters of probability and statistics , especially when the samples are rare
  与非贝叶扬方法相比，贝叶斯方法的特出特点是其学习机制可以综合先验信息和后验信息，既可避免只使用先验信息可能带来的主观偏见，和缺乏样本信息时的大量盲目搜索与计算，也可避免只使用样本信息带来的噪音的影响只要合理地确定先验，就可以进行有效的学习。因此，适用于具有概率统计特征的数据采掘和机器学习（或发现）问题，尤其是样本难得的问题