基于半监督FCM聚类算法的卫星云图分类

针对卫星云图的特点在分类特征集中采用了一种新的特征--差异化特征,该特征反映了云图的内部结构特点,并且具有良好的鲁棒性,能有效地避免云团位置变化对特征的影响.将半监督思想引入到模糊C均值聚类方法(FCM),克服了单纯的FCM方法未考虑领域知识导致的聚类结果的盲目性.半监督FCM方法在聚类过程中加入少量的由领域专家标记的样本,引入专家的领域知识,通过与这些带有类标记的样本进行相似性比较,引导FCM方法的聚类过程.试验结果表明,基于具有差异化特征的云图特征集,半监督FCM方法能有效地提高云图分类的准确率.     

英语教学中的师生非语言交际行为

介绍了非语言交际行为的定义、分类及作用,重点分析了英语教学中师生非语言交际行为及手段,对比点评了师生非语言交际行为的文化差异,认为:作为语言交际行为的补充,非语言交际行为在语言教学中发挥着不可低估的作用.     

基于自训练EM算法的半监督文本分类

为了提高计算效率,提出基于自训练的改进EM算法STEM。在每步迭代的E-step中,将中间分类器最有把握对其类别进行预测的未标注样本转移至标注样本集,并应用到M-step中进行下一个中间分类器的训练,从而引入了利用中间结果的自训练机制。文本分类实验表明STEM算法在大部分情况下的分类准确性都高于EM,并通过减少迭代提高了分类器学习的计算效率。     

基于神经网络的超精密机床伺服进给非线性模型辨识

以双轴Ｔ型结构的超精密金刚石车床的伺服进给系统为研究对象,采用自构造神经网络技术,建立了系统的非线性动态数学模型,为系统非线性控制与补偿提供参考模型     

THE CONCEPT OF NUCLEAR LEARNING

This article seeks to elucidate the concept of nuclear learning. It explores both the “nuclear” and the “learning” aspects of the concept. On the nuclear side, it distinguishes between learning basic facts about nuclear arms and drawing inferences about the larger implications of those facts. On the learning side, it discusses three issues: whether to use the term in a normative or value-neutral manner; the difference between learning that leads to a change in means versus learning that leads to a re-evaluation of ends; and whether learning only takes place at the level of individuals or whether there can also be learning by collective entities. The article argues there is no universal best answer to these questions and that the particular concept of learning that should be employed depends on the goals of the analyst. If the goal is to reduce the chances of nuclear war, however, one type of learning that will be important to consider is whether there is shared, cross-national learning.     

Learning in counterinsurgency: what do we really know?

Since the publication in 2002 of John Nagl's Learning to Eat Soup with a Knife, organizational learning has been widely presumed an important ingredient for success in counterinsurgency. But sampling the literature from before and after this time shows remarkably little analytical treatment of the issue of learning and even confusion over what it may mean. This article considers the theories, hypotheses, research strategies, threats to validity, methods of measurement, treatments of time, and general lack of statistical analysis in the work to date and recommends a course for future research.     

新疆石河子内初班学生的学习适应性研究

为深入了解内初班学生的学习适应性状况,本研究分别在石河子市四所内初班承办学校中,严格按照分层抽样的方法选取了294名学生作为被试,采用《学习适应性测验》量表探求内初班学生的学习适应性特征。结果显示:石河子内初班学生的学习适应性与全国常模基本一致,学习适应不良主要集中在学习技术和学习态度上。在性别上、年级上差异不显著,民考民和民考汉学生的学习适应性在全量表上差异显著,总体上民考民学生的学习适应性优于民考汉学生。     

利用深度学习的硬件计数器复用估计算法

利用深度学习方法,为硬件计数器复用(multiplexing,MPX)提供结果精度更高的估计模型。通过对MPX估计得到的结果与实际采集的真实数据进行相似性分析,证明相同程序多次运行之间得到的硬件计数值是线性相关的。采用神经网络多层感知器(multilayer perceptron,MLP)和双向门控神经网络(bidirectional gated recurrent unit, Bi-GRU)这2种深度学习模型,对MPX数据进行拟合。基于动态时间规整(dynamic time warping, DTW),提出一个全新的评估MPX数据精度的指标DTW-cost。实验结果表明,同时收集15个硬件事件数据时,MLP方法拟合得到的13个高性能计算应用平均准确率比现有使用最广的固定插值法高出10.53%,最多可提升19.8%;而在MLP表现较差的事件上,Bi-GRU方法得到的平均准确率提升了28.8%。     

Bayesian strategies for dynamic pricing in e‐commerce

E‐commerce platforms afford retailers unprecedented visibility into customer purchase behavior and provide an environment in which prices can be updated quickly and cheaply in response to changing market conditions. This study investigates dynamic pricing strategies for maximizing revenue in an Internet retail channel by actively learning customers' demand response to price. A general methodology is proposed for dynamically pricing information goods, as well as other nonperishable products for which inventory levels are not an essential consideration in pricing. A Bayesian model of demand uncertainty involving the Dirichlet distribution or a mixture of such distributions as a prior captures a wide range of beliefs about customer demand. We provide both analytic formulas and efficient approximation methods for updating these prior distributions after sales data have been observed. We then investigate several strategies for sequential pricing based on index functions that consider both the potential revenue and the information value of selecting prices. These strategies require a manageable amount of computation, are robust to many types of prior misspecification, and yield high revenues compared to static pricing and passive learning approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

无监督特征学习的人体活动识别

针对人的局限性可能会导致在提取特征中丢失重要信息,从而影响最终的识别效果问题,提出无监督特征学习技术的惯性传感器特征提取方法。其核心思想是使用无监督特征学习方法学习多个特征映射,再将所有特征映射拼接起来形成最终的特征计算方法。其优点是不会造成重要信息的损失,而且可以显著减少所使用的无监督特征学习模型的规模。为了验证所提出的特征提取方法在活动识别中的有效性,运用一个公开的活动识别数据集,使用三种常用无监督模型进行特征提取,并使用支持向量机进行活动识别。实验结果表明,特征提取方法取得了良好的效果,与其他方法相比具有一定的优势。