论寓教于乐的教学理念与设计

“寓教于乐”是一种理想的教育教学目标,也是大学教学改革的重点,教育游戏的出现为教学拓宽了道路,实践着“寓教于乐”的理念。通过参阅大量文献资料,对教育游戏的概念与现状进行了阐述,并提出了教育游戏的设计理念与策略,为教育游戏的设计提供指导和帮助。     

谈军校学报在院校科研活动中的能动作用

军校学报在院校的科研活动中不仅仅是学术交流的园地、成果展示的平台,同时,也在整合研究力量,引导学科建设;促进学术协作,推进研究深入;提高学术水平,优化作者队伍方面起着积极的作用。可以说军校学报在院校科研活动中发挥着积极的能动作用。     

《经济地理学》教学中研究性学习的探索

研究性学习是一种实践性较强的教育教学活动。文章阐述了研究性学习的内涵,明确了经济地理学课程教学中研究性学习的目标,归纳了经济地理学课程中研究性学习的内容,并提出了研究性学习的实施策略。     

一种基于神经网络的彩色图像分割方法

提出了适于彩色图像分割的加强型径向基函数网络方法,采用在线自适应聚类学习算法确定隐层节点的数目、激活函数中心值;通过Hebb学习算法迅速将隐层节点中心值分为目标颜色聚类中心和背景颜色聚类中心两类;输出层用竞争规则将目标与背景分开。通过对多幅彩色图像进行分割处理验证了该方法的有效性。     

An approximate dynamic programing approach to the development of heuristics for the scheduling of impatient jobs in a clearing system

A single server is faced with a collection of jobs of varying duration and urgency. Each job has a random lifetime during which it is available for nonpreemptive service. Should a job's lifetime expire before its service begins then it is lost from the system unserved. The goal is to schedule the jobs for service to maximize the expected number served to completion. Two heuristics have been proposed in the literature. One (labeled π^S) operates a static priority among the job classes and works well in a “no premature job loss” limit, whereas the second (π^M) is a myopic heuristic which works well when lifetimes are short. Both can exhibit poor performance for problems at some distance from the regimes for which they were designed. We develop a robustly good heuristic by an approximative approach to the application of a policy improvement step to the asymptotically optimal heuristic π^S, in which we use a fluid model to obtain an approximation for the value function of π^S. The performance of the proposed heuristic is investigated in an extensive numerical study. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

多模态交叉解耦的少样本学习方法

当前的多模态少样本学习方法忽视了属性间差异对正确识别样本类别的影响。针对这一问题,提出一种利用多模态交叉解耦的方法,通过解耦不同属性语义特征,并经过特征重建学习样本的本质类别特征,缓解类别属性差异对类别判别的影响。在两个属性差异较大的基准少样本数据集MIT-States和C-GQA上进行的大量实验表明,所提方法较现有方法有较大的性能提升,充分验证了方法的有效性,表明多模态交叉解耦的少样本学习方法能够提升识别少量测试样本的分类性能。     

A misclassification cost‐minimizing evolutionary–neural classification approach

Machine learning algorithms that incorporate misclassification costs have recently received considerable attention. In this paper, we use the principles of evolution to develop and test an evolutionary/genetic algorithm (GA)‐based neural approach that incorporates asymmetric Type I and Type II error costs. Using simulated, real‐world medical and financial data sets, we compare the results of the proposed approach with other statistical, mathematical, and machine learning approaches, which include statistical linear discriminant analysis, back‐propagation artificial neural network, integrated cost preference‐based linear mathematical programming‐based minimize squared deviations, linear integrated cost preference‐based GA, decision trees (C 5.0, and CART), and inexpensive classification with expensive tests algorithm. Our results indicate that the proposed approach incorporating asymmetric error costs results in equal or lower holdout sample misclassification cost when compared with the other statistical, mathematical, and machine learning misclassification cost‐minimizing approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.     

基于学习矢量量化(LVQ)神经网络的雷达体制识别

由于现代战争中雷达体制的多样化、复杂化及其综合应用使得雷达体制识别中要处理大量复杂的高维数据,学习矢量量化(LVQ)神经网络不仅能处理有监督分类,而且相对于其他神经网络能以较小的计算量处理大量输入数据,所以采用LVQ对雷达体制进行识别,同时针对LVQ学习速率的变化可能引起学习算法不稳定,采用修正的学习速率算法.在简要介绍雷达体制和LVQ的基础上构造了LVQ神经网络对雷达体制进行分类.通过与径向基神经网络(RBFN)识别算法的仿真对比,证实了方法的有效性.     

二语习得理论在大学英语教学中的应用

如何能把英语学好,这是英语学习者最为关心的问题。而英语教育工作者关注的问题则是,如何能把教学搞好,教学工作的核心是什么。根据二语习得理论,关键是采取多种教学方式,对学生进行有效的语言输入,给学习者建立一个理想的英语习得环境。     

普通高校非英语专业新生英语学习观念和策略的研究

本文以问卷和面谈的形式对非英语专业大学一年级新生作了一次调查,通过定量和定性分析发现:(1)学生对外语学习中的个体差异,外语学习的内容和形式,母语在外语学习中的作用没有明确的看法;(2)学生普遍认同管理学习过程的重要性;(3)学生的学习观念和学习策略之间存在着很大的差距。并探讨了此调查结果对外语策略培训的启示。