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group generation
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  “group generation”译为未确定词的双语例句
     AGV (Automated Guided Vehicle) optimization is a typical NP problem, in the course of initial group generation, this article applied accumulator process, crossover operator based continuous insertion heuristic mode, and push- connect-exclude method.
     AGV的优化调度属于典型的NP问题,在初始群体的产生中,采用聚加过程,并建立了可行插入的条件; 提出了基于连续插入启发式的交叉算子;
短句来源
     Newman and Ming yao Xu developed a new approach to metacyclic p groups suggested by a p group generation algorithm they found new representations for these groups [3-5] .
     NewmanandMingYaoXu发现了这些群的新的表示,给出了新的分类方法,这种方法是由p群生成算法(见文[3])得到的.
短句来源
     This paper gives another proof of these results, independent of the p group generation algorithm.
     本文的目的是给这些结果的另一种证明,与p群生成算法是不相关的.
短句来源
     Artillery Weapon-Target decision-making problem is NP hard,and is difficult to be brought into effect by general algorithms,Genetic algorithm is used to analyze and research Artillery Weapon-Target decision-making,The concrete steps include: Coding and ancestor group generation,calculating individual fitness degree of gene and selecting operation,intercross and mutation operation.
     炮兵火力决策问题是NP难题,用一般算法难以实现. 运用遗传算法对炮兵火力决策进行了分析研究,具体步骤包括:编码和生成祖先群体、计算基因个体适应度和选择操作、交叉及变异操作.
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  相似匹配句对
     group.
     对照组给予胃乃安口服,每次 4 粒,每日 3 次。
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     The P&T Group
     巴马丹拿集团
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     On "Generation
     “代”论
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     Discussion on Information Development for Electricity Generation Group
     发电集团信息化发展探讨
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     SU(3)_R GENERATION GAUGE GROUP
     SU(3)_g代规范群
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A finite p group P is called metacyclic if it has a cyclic normal subgroup A such that P/A is cyclic. King [2] classified metacyclic p groups. M.F.Newman and Ming yao Xu developed a new approach to metacyclic p groups suggested by a p group generation algorithm they found new representations for these groups [3-5] . This paper gives another proof of these results, independent of the p group generation algorithm.

一个有限p群p称为亚循环的,如果P有一个循环的正规子群A,使得PA是循环的.1973年King在文[2]中对亚循环p群进行了分类;在文[4,5]中M.F.NewmanandMingYaoXu发现了这些群的新的表示,给出了新的分类方法,这种方法是由p群生成算法(见文[3])得到的.本文的目的是给这些结果的另一种证明,与p群生成算法是不相关的.

Artillery Weapon-Target decision-making problem is NP hard,and is difficult to be brought into effect by general algorithms,Genetic algorithm is used to analyze and research Artillery Weapon-Target decision-making,The concrete steps include: Coding and ancestor group generation,calculating individual fitness degree of gene and selecting operation,intercross and mutation operation.Pass to impersonate the problem,more better of decision-making project is obtained.

炮兵火力决策问题是NP难题,用一般算法难以实现.运用遗传算法对炮兵火力决策进行了分析研究,具体步骤包括:编码和生成祖先群体、计算基因个体适应度和选择操作、交叉及变异操作.通过对这一问题的模拟仿真,获得了较优的决策方案.

Because of the lack of the adequate knowledge of users' interests and huge on-line computational demand,most existing document recommendation systems are not very effective and efficient.In this paper,a novel automatic personalized document recommendation system based on clustering and classification is proposed.The proposed system learns user profiles by employing machine learning methods and provides the personalized document recommendation services for each registered user based on the user profile.In order...

Because of the lack of the adequate knowledge of users' interests and huge on-line computational demand,most existing document recommendation systems are not very effective and efficient.In this paper,a novel automatic personalized document recommendation system based on clustering and classification is proposed.The proposed system learns user profiles by employing machine learning methods and provides the personalized document recommendation services for each registered user based on the user profile.In order to improve the recommendation's quality and reduce the on-line computation,the proposed system comprises off-line user profile and user group generation subsystem and on-line personalized document recommendation subsystem.The off-line user profile and user group generation subsystem clusters the documents into clusters called interest clusters,generates user profiles based on the interest clusters and clusters users into user groups based on the user profiles.The on-line personalized document recommendation subsystem classifies the new document and recommends the new document to the users who are interested in the interest cluster that the new document belongs to.The theoretical analysis and experimental results show that the system can improve the effectiveness and the real-time performance.The proposed idea and technique can also be used for other personalized information recommendation systems on the Internet.

由于缺乏足够的反映用户兴趣的知识,以及巨大的在线计算量,导致互联网上现有文章自动推荐系统普遍存在盲目性和低效性的问题.针对以上问题,提出了一种基于聚类和分类的个性化文章自动推荐系统,利用机器学习的方法隐式地获取用户模型,并根据用户模型为用户提供个性化的文章自动推荐服务.该系统包括离线用户模型及用户群获取子系统和在线个性化文章推荐子系统两大部分,前者对文章进行聚类形成聚类兴趣点,构建基于聚类兴趣点的用户模型,并根据用户兴趣聚类形成各兴趣点的用户群;后者对待推荐文章进行分类,搜索到其所属的兴趣点,向该兴趣点的用户群进行主动推荐.理论分析和实验结果表明,该系统能够显著提高有效性和在线响应速度.所述的设计思想和技术也适用于其它互联网个性化信息自动推荐系统.

 
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