Data mining is an application technology which involves many disciplines such as statistics and machine learning 数据挖掘是一门多学科交叉的应用技术，与机器学习和统计学紧密相关。
13 milo t , zohar s . using schema matching to simplify heterogeneous data translation . in proc . the 24th int 这一层即是调解器层，它利用一个规则集合，结合机器学习来匹配和集成模式中的要素。
In recent years , support vector machines ( svms ) have become increasingly popular techniques in machine learning 支持向量机是基于统计学习理论，借助最优化方法来解决机器学习问题的新工具。
At present , most of data mining algorithm is improved on some method in statistics and machine learning field 目前数据挖掘中的挖掘算法主要是对机器学习或统计分析等领域中的常用技术的改进。
The researcher of machine learning and expert system and neural biology provides a lot of classification methods 许多分类的方法已被机器学习、专家系统、统计学和神经生物学方面的研究者提出。
The third stage was from mid - 1970 to the beginning of 1980 ' s , in which machine learning went into its flourishing time 第三阶段是20世纪70年代中期到80年代初。该阶段是机器学习蓬勃发展的阶段。
The problem of how to combine machine learning theories into automated negotiation system has got more attention recently 因此，将机器学习理论应用到自动谈判系统中成为电子商务领域的最新研究课题。
Rough set theory is founded on indiscernibility relations and the common theory basis of this kind of machine learning 建立在不可区分关系上的粗糙集( roughset )理论为这一类型的机器学习提供了共同的理论基础。
This thesis tries to construct the translation model of ebmt automatically by using machine learning algorithm ( here , maximum entropy ) 本文利用机器学习算法(最大熵方法)对ebmt的翻译模型进行了自动构建尝试。
The main work at the beginning of 1970 ' s was to construct the machine learning system that can simulate human ' s concept acquision 第二阶段是20世纪70年代初。主要工作是建立各种模拟人的概念学习的机器学习系统。
