网络论坛教学评论的自动情感分析方法——以湖南商学院枫华论坛为例

校园网络论坛已经成为大学生实时发布各种观点和体会的公共平台。论坛产生的评论数据止以指数级的速度在增长,这些评论中包含了许多对教学管理有价值的信息,但是由于整个论坛的评论数量众多,很难用人工的方法抽取并且分析其中的教学评论情感信息,因此这些信息目前尚未得到管理者应有的关注。文章采用自动贝叶斯文本情感分类技术将校园网络论坛中的教学在线评论分成好评和差评情感信息,在湖南商学院枫华论坛上抽取的教学评论数据集上的分类准确度达到了87．3％,同时其结果的分布与人工标注结果的分布也不存在统计意义上的差异。该方法能够克服现有网上评教方法的一些缺陷,为教学管理部门实施动态教学管理提供了一种新的途径。     

多级分类映射方法在采油厂实时数据集成中的应用

实时数据集成是实现"数字化油田"的重要环节,它可以为生产过程的优化管理提供可靠的基础数据依据,其难点在于如何实现信息集成的标准化和信息源变化时系统的维护问题。在数据发送和接收的过程中采用了UDP技术,实现了实时数据传输的标准化,提出了多级分类映射概念,将系统的维护主动权下放到各站点,增强了信息源动态变化情况下系统的适应性和可维护性。     

基于松弛策略和多分类器的半监督高光谱图像分类

高光谱图像分类是高光谱遥感研究的热点之一.然而高光谱地物标签难以获取,包含噪音点等特征使得分类困难.基于不连续松弛和多分类器策略,本文提出了一种高光谱图像半监督分类方法,可以达到去噪和改善分类精度的目的.实验结果编码,在有限标记样本的情况下,所提出方法能够获得良好的分类结果.     

Boosting算法及其在动态视频图像中的应用

Boosting是一种有效的分类器组合方法,它用某个分类算法生成一系列的基分类器,每个基分类器的训练依赖于在其之前产生的分类器的分类结果,基分类器在训练集上的错误率用于调整训练样本的概率分布,最终分类器通过单个基分类器的加权投票建立起来。将Boosting算法应用在动态车型图像检测中,大大提高了对运动过程中车辆的识别能力,对智能交通系统的发展起着推动作用。     

Multi-valued Document Classification Based on Generalized Bradley-Terry Classifiers Utilizing Accuracy Information

Due to the development of computer network, a large amount of documents are treated in many fields. The number of digital document data stored in databases is enormous, accordingly it is difficult for analysts to read all documents and classify it by hand. Therefore, it is necessary to develop the technology of automatic document classification by using computers these days. From the above needs, many classifiers with good performance have been proposed, i.e., Relevance Vector Machine （RVM） and Support Vector Machine （SVM） that are known as good binary classifiers. For multi-valued document classification problems, it is known that a multi-valued classifier by combining several binary classifiers has a good performance. In this study, the method to construct an efficient combination of binary classifiers based on improving Generalized Bradley-Terry （GBT） model, which has high extensibility, is focused. This model is an expansion of Bradley-Terry （BT） model. Though the BT model has a limitation on combination of classes, the GBT model enables us to utilize any binary classifier which classifies into two arbitrary subsets in the class set. Generally, when several binary classifiers learn from the training dataset, there would be the difference of accuracy between these binary classifiers, due to the existence of categories that cannot be easily classified. However, the conventional method of multi-valued classification by GBT binary classifiers does not take the accuracy of each classifier into consideration. To avoid this problem, a new way of multi-valued classification method by considering each classifier＇s accuracy is proposed. The purpose of this study is to construct a good multi-valued classifier by calculating the accuracy of each classifier and utilizing it as the weight. In order to verify the effectiveness of the proposed method, the simulation experiment by using newspaper articles is conducted.     

“数＋量”结构拼写分合谈

汉语数词与量词的分合是个缠绕不清的问题,常给分词连写带来困扰,这是因为数词和量词各是一个半自足的系统,形成了两者相互依存而又可分可合的基本格局,当两者都处在不自足的情况下,便结合为“数量词”,但只要其中一方能够自足,则可分为“数词”与“量词”,“数＋量”结构分词调查结果显示,使用者通常是依据形式或意义是否自足来感知词的,对双音结构的感知主要依凭“双字格”的构词模式进行类推,对三音结构的感知一般根据意义联系的密切程序决定分合。     

A stochastic comparison of customer classifiers with an application to customer attrition in commercial banking

The classification of clients is an essential matter in commercial banking, insurance companies, electrical corporations, communication business, etc. Those companies frequently classify their customers by means of the information provided by the so-called classifier. Motivated by the need to compare systems of classification, we introduce a new stochastic order which permits the comparison of classifiers. The stochastic order is analysed in detail, providing characterizations and properties as well as connections with other stochastic orders and other classification systems. Such an order is applied to compare some classifiers used by a Spanish commercial banking to analyse the key problem of customer churn, obtaining conclusive results by means of real databases. Namely, the optimal classifier among them in the new stochastic order is obtained.     

基于最大似然分类算法的土地覆盖分类精度控制研究

在土地覆盖分类应用中执行最大似然分类算法存在许多可能影响精度的问题，包括确定分类类别及数量、光谱波段、先验概率、选择和改进样本区等。在总结了这些问题后，提出了解决方法，并应用ERDAS IMAGINE 8．6软件以南京市TM数据为例进行了分类实验，结果证明此方法能有效提高分类精度。