进化式信息过滤方法研究

进化式信息过滤方法从多个角度描述用户的信息需求,通过类似自然选择的过程,达到系统整体过滤性能的优化.同时还从面向对象程序设计语言的设计思想中获得启发,引入了继承、类树的概念,增加了过滤系统的易用性.这种方法可以缩短训练时间,提高过滤质量,同时减小过滤结果与训练次序的相关性.     

A structure-based approach for multimedia information filtering

While multimedia documents are sequentially presented to users, an information filtering (IF) system is useful to achieve a good retrieval performance in terms of both quality and efficiency. Conventional approaches for designing an IF system are based on the user's evaluation on information relevance degree (IRD), but ignore other attributes in system design such as relative importance of the data in a collection of multimedia documents. In this paper, we aim at developing a framework of designing structure-based multimedia IF systems, which incorporates the characteristics of the importance and relevance of multimedia documents. A method of calculating the values of relative importance degree of multimedia documents is proposed. Furthermore, these values are combined into the IRD of multimedia documents to improve the representation of user profiles. An illustrative example is given to demonstrate the proposed techniques.     

Information Filtering: Overview of Issues,Research and Systems

An abundant amount of information is created and delivered over electronic media. Users risk becoming overwhelmed by the flow of information, and they lack adequate tools to help them manage the situation. Information filtering (IF) is one of the methods that is rapidly evolving to manage large information flows. The aim of IF is to expose users to only information that is relevant to them. Many IF systems have been developed in recent years for various application domains. Some examples of filtering applications are: filters for search results on the internet that are employed in the Internet software, personal e-mail filters based on personal profiles, listservers or newsgroups filters for groups or individuals, browser filters that block non-valuable information, filters designed to give children access them only to suitable pages, filters for e-commerce applications that address products and promotions to potential customers only, and many more. The different systems use various methods, concepts, and techniques from diverse research areas like: Information Retrieval, Artificial Intelligence, or Behavioral Science. Various systems cover different scope, have divergent functionality, and various platforms. There are many systems of widely varying philosophies, but all share the goal of automatically directing the most valuable information to users in accordance with their User Model, and of helping them use their limited reading time most optimally. This paper clarifies the difference between IF systems and related systems, such as information retrieval (IR) systems, or Extraction systems. The paper defines a framework to classify IF systems according to several parameters, and illustrates the approach with commercial and academic systems. The paper describes the underlying concepts of IF systems and the techniques that are used to implement them. It discusses methods and measurements that are used for evaluation of IF systems and limitations of the current systems. In the conclusion we present research issues in the Information Filtering research arena, such as user modeling, evaluation standardization and integration with digital libraries and Web repositories.     

基于协同演化遗传算法的文本特征获取方法

信息过滤技术是解决“信息过载”和“信息迷向”问题的有效手段。为高效地确立用户的信息需求模型,提出利用协同演化的遗传算法解决多主题多文本的特征获取问题。协同演化遗传算法根据种群中个体进化速度、效果的不同,采取相互评价、相互学习、群体进化的协同演化策略,使得个体在其它种群、个体的指导下,不断获得较好的基因,从而实现文本特征的抽取。实验验证了方法的有效性。     

A conceptual definition of a holonic processing framework to support the design of information fusion systems

This paper proposes a conceptual definition of an information fusion (IF) processing framework. Several concepts borrowed from complex systems theory, informational philosophy and computer sciences have been integrated to conceptualize that framework. The concepts of holon and informon developed by Koestler, Sulis, Alonso, Paggi et al. are exploited here to develop an information fusion processing framework. The proposed functional holonic structure is suitable for processing any level of information abstraction of the Joint Directors of Laboratory (JDL) data fusion model. The framework comprises the characterization of a basic element of information and the definition of an IF cell as a basic IF system unit to achieve fusion of information. The framework advocates a goal-driven approach with notions coming from business sciences to take into account quality of information for managing the fusion process. The framework is illustrated through several examples namely with an elaborated case in remote sensing.     

Quality-driven information filtering using the WIQA policy framework

Web-based information systems, such as search engines, news portals, and community sites, provide access to information originating from numerous information providers. The quality of provided information varies as information providers have different levels of knowledge and different intentions. Users of web-based systems are therefore confronted with the increasingly difficult task of selecting high-quality information from the vast amount of web-accessible information. How can information systems support users to distinguish high-quality from low-quality information? Which filtering mechanisms can be used to suppress low-quality information? How can filtering decisions be explained to the user? This article identifies information quality problems that arise in the context of web-based systems, and gives an overview of quality indicators as well as information quality assessment metrics for web-based systems. Afterwards, we introduce the WIQA—Information Quality Assessment Framework. The framework enables information consumers to apply a wide range of policies to filter information. The framework employs the Named Graphs data model for the representation of information together with quality-related meta-information. The framework uses the WIQA-PL policy language for expressing information filtering policies against this data model. WIQA-PL policies are expressed in the form of graph patterns and filter conditions. This allows the compact representation of policies that rely on complex meta-information such as provenance chains or combinations of provenance information and background information about information providers. In order to facilitate the information consumers’ understanding of filtering decisions, the framework generates explanations of why information satisfies a specific policy.     

信息安全从业人员技能培训体系的构建

本文从影响信息安全从业人员技能培训的因素出发,从法制、组织与机构、师资、教材、信息化、档案管理六个方面构建了信息安全从业人员技能培训体系,并通过质量监督、质量评估及意见反馈的方式保证了信息安全从业人员技能培训体系的质量.     

结合模糊聚类的遗传算法在网络信息过滤中的应用

在基于遗传算法的信息过滤系统中引入模糊聚类思想,对种群中的每个个体进行模糊相似矩阵直接聚类,然后根据聚类的结果采用所提出的适应度函数来评估种群的适应度,通过迭代训练得出更准确的用户兴趣模板,从而提高了信息过滤的准确率。并且将该方法应用到了所设计的网络信息过滤系统中,进行了验证。     

Rough set-aided keyword reduction for text categorization

The volume of electronically stored information increases exponentially as the state of the art progresses. Automated information filtering (IF) and information retrieval (IR) systems are therefore acquiring rapidly increasing prominence. However, such systems sacrifice efficiency to boost effectiveness. Such systems typically have to cope with sets of vectors of many tens of thousands of dimensions. Rough set (RS) theory can be applied to reducing the dimensionality of data used in IF/IR tasks, by providing a measure of the information content of datasets with respect to a given classification. This can aid IF/IR systems that rely on the acquisition of large numbers of term weights or other measures of relevance. This article investigates the applicability of RS theory to the IF/IR application domain and compares this applicability with respect to various existing TC techniques. The ability of the approach to generalize, given a minimum of training data is also addressed. The background of RS theory is presented, with an illustrative example to demonstrate the operation of the RS-based dimensionality reduction. A modular system is proposed which allows the integration of this technique with a large variety of different IF/IR approaches. The example application, categorization of E-mail messages, is described. Systematic experiments and their results are reported and analyzed.     

快速信息融合Kalman滤波器

应用现代时间序列分析方法,在标量加权线性最小方差融合准则下,提出一种多传感器快速信息融合稳态Kalman滤波器.基于ARMA新息模型计算稳态Kalman滤波器增益,提出了计算传感器之间的滤波误差方差阵和协方差阵的Lyapunov方程,它可用迭代法求解,并证明了迭代解的指数收敛性.与基于Riccati方程按矩阵加权的信息融合Kalman滤波器相比,可明显减小计算负担,便于实时应用,可用于设计含未知噪声统计系统的信息融合自校正Kalman滤波器.最后以目标跟踪系统的一个仿真例子说明了其有效性.     

一种改进的自适应文本信息过滤模型

自适应信息过滤技术能够帮助用户从Web等信息海洋中获得感兴趣的内容或过滤无关垃圾信息．针对现有自适应过滤系统的不足,提出了一种改进的自适应文本信息过滤模型．模型中提供了两种相关性检索机制,在此基础上改进了反馈算法,并采用了增量训练的思想,对过滤中的自适应学习机制也提出了新的算法．基于本模型的系统在相关领域的国际评测中取得良好成绩．试验数据说明各项改进是有效的,新模型具有更高的性能．     

信息检索与过滤中的信息需求表示方法

信息需求的表示方法是影响信息检索和信息过滤结果的重要因素。介绍了一些基本的信息需求表示方法，并对各种表示方法对信息检索和信息过滤结果产生的影响进行了分析和比较，最后提出了改进这些方法的一些思想。     

基于过滤机制筛选信息的多智能体策略方法

多智能体系统在进行协作或竞争时,会面临联合信息空间扩大、智能体间信息提取效率降低的问题.对此,采用增加过滤机制来筛选信息的多智能体强化学习策略方法(FMAC),以增强智能体间信息交流能力.该方法通过找到彼此相关联的智能体,根据相关性计算智能体的信息贡献,过滤掉无关智能体信息,从而实现在合作、竞争或者混合环境下智能体间有...     

优化稀疏数据集提高协同过滤推荐系统质量的方法

协同过滤是目前个性化推荐系统中效果较好的一种推荐技术。由于用户和项目数量的急剧增加,使得反映用户喜好信息的评分矩阵非常稀疏,严重影响了协同过滤技术的推荐质量。针对这一问题提出了综合均值优化填充方法,该方法相比较于缺省值法和众数法,考虑到了用户评分尺度问题,同时也不存在众数法中的“多众数”和“无众数”问题。在同一数据集上,通过使用传统的基于用户的协同过滤算法进行验证,表明此方法可以有效提高推荐系统的推荐质量。     

基于内容的网页信息处理方法

提出了一种基于内容的网页信息处理方法：利用顺序滤波直接对网页页面内容进行过滤处理，再根据设置的阈值将过滤后的网页页面变为黑白页面，从而提取出网页中的图像信息。对多个包含图像信息的网页进行处理的结果表明此方法能较有效的提取出网页中的图像信息。基于此方法和网页文本信息提取方法的不同原理，还尝试构建了一种基于内容的网页信息处理系统。     

基于改进型变分自编码器的不确定性空间信息重建方法

不确定性空间信息在众多科学领域得到了广泛应用。然而目前用于不确定性空间信息重建的方法需要多次对训练图像（TI）进行扫描,再通过复杂的概率计算获得模拟结果,导致这些方法的效率较低,且模拟过程复杂。针对这一问题,提出了将费雪信息量和变分自编码器（VAE）结合应用于不确定性空间信息的重建。首先,通过编码器神经网络对空间信息的结构特征进行学习,并训练得到空间信息的均值和方差;然后,进行随机采样,根据采样结果和空间信息的均值、方差重建中间结果,并将编码器神经网络的优化函数与费雪信息量相结合来优化网络;最后,将中间结果输入解码器神经网络中,以对空间信息进行解码重建,并将解码器的优化函数与费雪信息量结合对重建结果进行优化。通过比较各方法重建结果与训练数据的多点连通曲线、变差函数、孔隙分布和孔隙度表明,所提方法的重建质量比其他方法的更好。具体来说,该方法重建结果的平均孔隙度为0.171 5,与其他方法重建结果的平均孔隙度更接近训练数据的孔隙度0.170 5。且相较于传统方法,其平均CPU利用率从90%下降到25%,平均内存占用下降了50%,说明该方法的重建效率更高。而通过重建质量和重建效率两个方面的对比,说明了该方法的有效性。     

一种基于粗集理论的文本特征约简新方法

信息过滤技术是解决“信息过载”和“信息迷向”问题的有效手段。为了高效地确立用户的信息需求模型,在粗集理论属性约简技术的基础上,提出主题特征选择的新方法RSAR。RSAR方法有效地克服了传统粗集方法不能直接处理连续值属性的缺陷,依据相对核和属性重要度从示例文本中抽取特征词,从而建立用户模型。实验验证了RSAR方法的有效性。     

一种基于奇异值分解的双语信息过滤算法

本文提出了一种基于SVD(奇异值分解)的双语信息过滤算法,将双语文档进行了统一的表示,使得适应于单语过滤的算法可以方便地用于双语过滤,同时对文档向量进行了压缩,滤去了噪声。在应用方面,将双语过滤算法用于互联网上的个性化主动信息过滤。     

带约束本体语义扩展的信息过滤算法

提出一种基于本体的信息过滤方法。该方法通过本体实现形式化语义描述,并对原始输入条件进行带约束规则的本体语义扩展。进而为了实现语义匹配,给出了信息向量语义描述及权重计算方法。最终,实现基于语义相似度计算的信息过滤。实验证明,该方法是有效的。     

一种不良信息过滤的文本预处理方法研究

目前互联网上含有不良内容的文本信息形式多变,本文主要针对不良内容的敏感信息出现的特征变化,提出一种基于文本内容的不良信息过滤的文本预处理方案,并着重探讨了其结构变化的敏感信息的识别及解决的方法。研究表明在文本的分词处理前,对不良信息形式的变化进行预处理,能够提高过滤的效率。