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991.
高等院校中国古代文学课程已经形成了一个比较完善的学科体系,其所传授的知识内容、教学的模式也已相对固定.但由于高校中国古代文学教学课时数相对较少、教学内容庞杂、学生学习方法不当等诸多原因,学生在学习时面临不少的困惑. 相似文献
992.
Collaborative Filtering (CF) is a commonly used technique in recommendation systems. It can promote items of interest to a target user from a large selection of available items. It is divided into two broad classes: memory-based algorithms and model-based algorithms. The latter requires some time to build a model but recommends online items quickly, while the former is time-consuming but does not require pre-building time. Considering the shortcomings of the two types of algorithms, we propose a novel Community-based User domain Collaborative Recommendation Algorithm (CUCRA). The idea comes from the fact that recommendations are usually made by users with similar preferences. The first step is to build a user-user social network based on users’ preference data. The second step is to find communities with similar user preferences using a community detective algorithm. Finally, items are recommended to users by applying collaborative filtering on communities. Because we recommend items to users in communities instead of to an entire social network, the method has perfect online performance. Applying this method to a collaborative tagging system, experimental results show that the recommendation accuracy of CUCRA is relatively good, and the online time-complexity reduces to O.n/. 相似文献
993.
张嘉昇 《江西科技师范学院学报》2013,(1):105-109
社会转型带来人们生活方式和思想观念的深刻变化,改变了思想政治教育的大环境。落后陈旧的教育理念,无法应对学生思想认识的变化,造成思想政治教育缺乏针对性与实效性。只有更新观念,转换视角,才能准确把握思想政治教育的现状和学生实际,找到行之有效的解决问题的方法与对策,真正发挥思想政治教育的重要作用。 相似文献
994.
本文以黑河学院为例,对中俄高校体育竞赛运作情况进行调研,调查高校以体育竞赛为戴体的校园文化建设现状,发现存在的问题,并提出解决问题的建设性意见,为今后高校体育竞赛与校园文化建设的良性互动提供理论依据,具有现实意义。 相似文献
995.
996.
随着计算机技术的日益成熟和网络应用的日渐鲁遍,云计算概念开始慢慢进入人们的视野,并且逐步应用到生活生产的各个领域,对于国家教育部提出的建设数字化高校信息网络,云计算无疑具有巨大的发挥前景和应用空间.本文就如何搭建云计算平台,如何高效快捷的整合信息资源,更好的为广大高校师生服务,提出了设计云计算的一站式服务解决方案. 相似文献
997.
提出了一种基于循环回归的推荐算法.首先,对原数据集中的评分数据及缺失值进行离散化处理,然后对离散化数据进行回归模型训练,此过程循环执行并最终建立推荐系统.在离散化阶段,对比不同的离散方法,并对它们的分类粒度开展研究.在模型训练阶段,讨论回归算法对于模型性能的影响.数值计算实验表明,本算法较之近年非常热门的SVDFeaute方法,能够产生更小的均方根误差,验证了算法的有效性. 相似文献
998.
蛋白质相互作用关系对理解生物过程具有非常重要的意义,为了解决同位语依存关系带来的噪音干扰,提出了一个改进的基于树核的PPI提取方法,通过定义一些相关的处理规则来有效优化两个蛋白质之间的最短依存路径,在此基础上,用有效优化路径来指导成分句法树的裁剪,使得用于PPI提取的成分树更加精确和简洁.实验结果表明:用有效优化路径指导的成分句法树在五个常用的语料库上都取得了较好的效果. 相似文献
999.
Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous (i.e., dirty) data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis (PCA) model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and then guide the error detection to discover any anomaly observations. We conducted simulations for each of the proposed models, and evaluated the performance by deriving the Receiver Operating Characteristic (ROC) curves. 相似文献
1000.
With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests’ relationship, we not only consider users’ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model (I-FGM) to incorporate these factors. Experiments on two data sets (bookmarking and music network) demonstrate that this predictive method can achieve better results than the other three methods (ANN, NB, and SVM). 相似文献