煤炭高校环境学科人才培养模式及思考     

刘汉湖  白向玉  裴宗平 《煤炭高等教育》2012,30(3):67-70

随着我国国民经济的快速发展,煤炭需求总量不断增加,资源、环境和安全压力进一步加大。煤炭高校担负着服务煤炭行业和地方经济社会发展的双重任务,为了解决煤炭开采对环境的破坏和污染问题,满足煤炭工业可持续发展和＂绿色矿业＂对环境学科人才需求,煤炭高校应该对学科结构和人才培养模式进行调整,构建＂厚基础、宽口径、重能力、有矿业特色＂的环境学科培养模式和课程体系。  相似文献   

矿业类本科生全程化就业指导体系探索     

高宝全 《煤炭高等教育》2012,30(3):74-76

全程化就业指导是新时期就业工作的迫切需要,矿业特色院校应以就业指导工作的转型为保障,针对不同年级大学生的身心与职业发展特点,分年级、分阶段进行指导,并将就业指导工作贯穿于学校教育的每个环节。  相似文献   

基于Apriori算法的图书馆多维数据挖掘算法研究     

马文庆 《佳木斯教育学院学报》2012,(11):467+469

本文在研究了多维关联规则数据挖掘的理论及方法的基础上,深入分析以往算法的优缺点并结合图书馆行业管理信息系统的特点,选择对Apriori算法结合数据立方体技术进行改进和优化,形成了适合利用多维关联规则对图书馆数据进行挖掘的新算法。  相似文献   

关联规则算法的实现与优化     

王晶  赵志强 《实验技术与管理》2012,29(8):111-112

对基于关联规则的数据挖掘算法进行了研究,对经典的频繁项集计数算法进行了改进,提高了关联规则数据挖掘的效率。优化结果证明了关联规则算法在医学科研实验室数据挖掘中的重要作用。  相似文献   

支持向量机的研究与应用     

胡运红 《运城学院学报》2012,30(2):4-7

通过对支持向量机的历史发展和理论进行分析,阐述了支持向量机相对于以往机器学习算法的优点。通过分析支持向量机的国内外研究现状,对该理论和算法的应用与发展前景进行了探讨。  相似文献   

对称图法化简逻辑函数之对称方形图法     

左安元  罗府 《毕节学院学报》2012,30(4):103-106

使用一种新的逻辑函数化简的图形法,使得化简5变量及以上的逻辑函数变得简单、直观、容易操作。这种对称方形图法化简方法采用方形图的对称性并在格雷码中找到一种既能满足最小项逻辑相邻,又能保证最小项对称相邻并符合方形图的对称性质的编码。化简过程则是根据方形图的对称性找出所有相邻的最小项,从而消掉n个变化的量,保留(m-n)个不变的量,最后将输出结果表示为与或式得到最终结果。这种化简方法对于任意变量的逻辑函数都适用并且可以将复杂度减少到最小,清晰度提升到一定的高度。  相似文献   

基于数据挖掘的课程管理信息系统研究     

刘勘  甘小亚 《湖北第二师范学院学报》2012,(8):31-34

本文设计了一个基于数据挖掘的课程管理系统,采用了关联规则、分类、聚类等几种主要的数据挖掘方法,对学生学习过程中各类数据进行分析,挖掘学生成绩与教学过程中各类课堂数据之间隐藏的关系,为教师教学及学生日后的学习提供指导。  相似文献   

数据挖掘技术在高职生成绩分析与预测中的应用     

戚冬冬 《安徽职业技术学院学报》2013,(4):13-16

作为能对数据进行有效分析与预测的数据挖掘技术,它的关联规则已经被教育界广泛应用于各类数据处理与分析的实践,尤其是对学生的成绩进行分析与预测。文章以江苏联合职业技术学院连云港财经分院2011级会计电算化专业学生的专业基础课和专业方向课成绩的分析与预测为例,结果发现：该院会计电算化专业学生的专业方向课的成绩略高于专业基础课,而在专业基础课成绩不及格的学生中,只有两成学生的专业方向课成绩通过努力才能及格,且取得“良”或是“良”以上的成绩的可能性极低。  相似文献   

Robust graph learning with graph convolutional network     

《Information processing & management》2022,59(3):102916

Graph convolutional network (GCN) is a powerful tool to process the graph data and has achieved satisfactory performance in the task of node classification. In general, GCN uses a fixed graph to guide the graph convolutional operation. However, the fixed graph from the original feature space may contain noises or outliers, which may degrade the effectiveness of GCN. To address this issue, in this paper, we propose a robust graph learning convolutional network (RGLCN). Specifically, we design a robust graph learning model based on the sparse constraint and strong connectivity constraint to achieve the smoothness of the graph learning. In addition, we introduce graph learning model into GCN to explore the representative information, aiming to learning a high-quality graph for the downstream task. Experiments on citation network datasets show that the proposed RGLCN outperforms the existing comparison methods with respect to the task of node classification.  相似文献   

HGNN: Hyperedge-based graph neural network for MOOC Course Recommendation     

《Information processing & management》2022,59(3):102938

Previous studies on Course Recommendation (CR) mainly focus on investigating the sequential relationships among courses (RNN is applied) and fail to learn the similarity relationships among learners. Moreover, existing RNN-based methods can only model courses’ short-term sequential patterns due to the inherent shortcomings of RNNs. In light of the above issues, we develop a hyperedge-based graph neural network, namely HGNN, for CR. Specifically, (1) to model the relationships among learners, we treat learners (i.e., hyperedges) as the sets of courses in a hypergraph, and convert the task of learning learners’ representations to induce the embeddings for hyperedges, where a hyperedge-based graph attention network is further proposed. (2) To simultaneously consider courses’ long-term and short-term sequential relationships, we first construct a course sequential graph across learners, and learn courses’ representations via a modified graph attention network. Then, we feed the learned representations into a GRU-based sequence encoder to infer their short-term patterns, and deem the last hidden state as the learned sequence-level learner embedding. After that, we obtain the learners’ final representations by a product pooling operation to retain features from different latent spaces, and optimize a cross-entropy loss to make recommendations. To evaluate our proposed solution HGNN, we conduct extensive experiments on two real-world datasets, XuetangX and MovieLens. We conduct experiments on MovieLens to prove the extensibility of our solution on other collections. From the experimental results, we can find that HGNN evidently outperforms other recent CR methods on both datasets, achieving 11.96% on P@20, 16.01% on NDCG@20, and 27.62% on MRR@20 on XuetangX, demonstrating the effectiveness of studying CR in a hypergraph, and the importance of considering both long-term and short-term sequential patterns of courses.  相似文献