Co-Concept-Boosting视频语义索引方法

语义概念探测是建立视频语义索引的根本方法,传统探测方法没有充分考虑语义概念间存在的复杂关系.本文充分利用概念间关系提出了co-concept-boosting方法,该方法分为三个层次:第一层是基于上下文关系的探测模型的构建,第二层是结合概念间关系的boosting处理,第三层是对boosting过程中产生的多个探测模型的融合.利用Trecvid2005数据的实验分析证明,该方法具有良好的性能以及稳定性.     

Web Classification Based on Latent Semantic Indexing

A new web document automatic classification algorithm based on Latent Semantic Indexing （LSIWAC）, is proposed in this paper. LSMAC uses the LSI based on Singular Value Decomposition （SVD） to compress the document vector space to lower dimensional space. Using the optimal clustering, LSIWAC can cluster part of web documents Then, LSIWAC uses the optimal discriminate transform to get feature vector from every clustering＇s discriminate features. Finally, it uses the conception classification algorithm to classify the rest documents. LSIWAC solves the high dimension problem and improves the precision of web classification.     

基于潜在语义标引的WEB文档自动分类

Web挖掘技术在商业上有广泛的应用前景,但现有的Web挖掘技术存在计算量大,精度不高等问题。论文提出的LSIWAC算法,首先运用潜在语义标引技术将Web页面词空间压缩到低维的特征空间;然后,在得到的特征空间上运用最优聚类将样本集合分为若干簇;对得到的每簇鉴别特征再利用最佳鉴别变换进行压缩和特征抽取,并用最终得到的特征矢量进行分类。该方法克服了样本高维效应,有效提高分类准确率,降低计算量。实验结果验证所提方法的有效性。     

基于Rough集潜在语义索引的Web文档分类

Rough集(粗糙集)埋论是一种处理不确定或模糊知识的数学工具。提出了一种基于Rough集理论的潜在语义索引的Web文档分类方法。首先应用向量空间模型表示Web文档信息,然后通过矩阵的奇异值分解来进行信息过滤和潜在语义索引;运用属性约简算法生成分类规则,最后利用多知识库进行文档分类。通过试验比较,该方法具有较好的分类效果。     

Semantic Image and Video Indexing in Broad Domains

The six papers in this special section focus on semantic image and video indexing in broad domains. To bring semantics to the user in broad domains both the indexing and retrieval step have to be considered. The papers here address both steps and the relation to ontologies.     

Semantic Naturalization via Interactive Perceptual Causality

A novel semantic naturalization program is proposed. Its three main differences from informational semantics approaches are as follows. First, it makes use of a perceptually based, four-factor interactive causal relation in place of a simple nomic covariance relation. Second, it does not attempt to globally naturalize all semantic concepts, but instead it appeals to a broadly realist interpretation of natural science, in which the concept of propositional truth is off-limits to naturalization attempts. And third, it treats all semantic concepts as being purely abstract, so that concrete cognitive states are only indexed by them rather than instantiating them.

基于潜在语义索引和句子聚类的中文自动文摘

自动文摘是自然语言处理领域的一项重要的研究课题.提出一种基于潜在语义索引和句子聚类的中文自动文摘方法.该方法的特色在于：使用潜在语义索引计算句子的相似度,并将层次聚类算法和K-中心聚类算法相结合进行句子聚类,这样提高了句子相似度计算和主题划分的准确性,有利于生成的文摘在全面覆盖文档主题的同时减少自身的冗余.实验结果验证了该文提出的方法的有效性,对比传统的基于聚类的自动文摘方法,该方法生成的文摘质量获得了显著的提高.     

基于流形空间的交互式人脸图像索引

认知科学表明基于流形学习的人脸图像检索能准确反映人脸图片的内在相似性和人类的视觉感知本质. 提出一种基于相关反馈的人脸高维索引方法--NDL,以提高人脸图像检索的性能.同时在该索引基础上提出一种流形空间下的相似查询--虚拟k近邻查询(Vk-NN), 该查询方法特别为基于NDL的人脸检索而设计.首先通过在一定阈值约束下计算任何两个人脸图片的相似度,建立一个称为邻接距离表(NDL)的二维距离图. 同时将距离值用B+-树建立索引.最后, 高维流形空间的Vk-NN查询转化为一维空间的基于B+树的查询. 实验表明:NDL索引在流形空间的检索效率明显优于顺序检索,特别适合海量人脸图片的检索.     

On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing

Non-negative Matrix Factorization (NMF) and Probabilistic Latent Semantic Indexing (PLSI) have been successfully applied to document clustering recently. In this paper, we show that PLSI and NMF (with the I-divergence objective function) optimize the same objective function, although PLSI and NMF are different algorithms as verified by experiments. This provides a theoretical basis for a new hybrid method that runs PLSI and NMF alternatively, each jumping out of the local minima of the other method successively, thus achieving a better final solution. Extensive experiments on five real-life datasets show relations between NMF and PLSI, and indicate that the hybrid method leads to significant improvements over NMF-only or PLSI-only methods. We also show that at first-order approximation, NMF is identical to the χ²-statistic.     

基于本体和语义标引的地质资料服务与管理研究

在信息化时代,高效地组织管理大量地质领域数据成为实现良好的地质资料服务与管理的关键所在。为解决我国地质资料标准化程度低及应用难度大等问题,本文提出了一种基于本体和语义标引的地质资料服务与管理方法。首先建立地质领域信息资源元数据模型和关联关系模型;并根据关联模型自动化构建计算机可读的地质本体;采用基于地质本体的语义概念映射的方法将高维的词语向量转换到低维语义空间,加之词频、词位置和词跨度等权重因子的综合作用,对地质资料文本进行精准快速有效的分类,实现文本的语义标引;最后根据地质本体和语义标引开展地质资料检索试验,试验结果表明,查全率和查准率分别从75.7%和81.2%提高到80.7%和84.8%,检索效果得到了明显提升。因此,本文为地质资料的服务与管理模型设计与实现奠定了良好的基础。     

基于证据理论的视频语义概念检测

视频语义概念检测是跨越语义鸿沟问题,实现基于语义的视频检索的前提。本文提出了一种基于证据理论的视频语义概念检测方法。首先,分别提取了镜头关键帧的分块颜色矩、小波纹理特征和边缘方向直方图特征;然后,利用支持向量机(Support vector machine,SVM)对3种特征数据分别进行训练,分别建立分类器模型;再次,对各SVM模型泛化误差进行分析,采用折扣系数法对不同SVM模型输出的分类结果进行修正;最后,采用证据融合公式对修正后的输出进行融合,把融合结果作为最终的概念检测结果。实验结果表明,新方法提高了概念检测的准确率,优于传统的线性分类器融合方法。     

LSISOM — A Latent Semantic Indexing Approach to Self-Organizing Maps of Document Collections

The Self Organizing Map (SOM) algorithm has been utilized, with much success, in a variety of applications for the automatic organization of full-text document collections. A great advantage of the SOM method is that document collections can be ordered in such a way so that documents with similar content are positioned at nearby locations of the 2-dimensional SOM lattice. The resulting ordered map thus presents a general view of the document collection which helps the exploration of information contained in the whole document space. The most notable example of such an application is the WEBSOM method where the document collection is ordered onto a map by utilizing word category histograms for representing the documents data vectors. In this paper, we introduce the LSISOM method which resembles WEBSOM in the sense that the document maps are generated from word category histograms rather than simple histograms of the words. However, a major difference between the two methods is that in WEBSOM the word category histograms are formed using statistical information of short word contexts whereas in LSISOM these histograms are obtained from the SOM clustering of the Latent Semantic Indexing representation of document terms.     

基于LSI和SVM分类法的定题邮件过滤研究

潜在语义索引(LSI)是一种有效的信息查询方法,同时也被成功地应用到了文本分类中。LSI能解决同义和多义的问题,通过降低原始文档-术语矩阵的噪声来凸现出词条和文档之间的语义关系。为了识别和过滤有害的、不期望的定题的信息或Email,在双语言环境下(包括中文和英文),提出了一个基于改进的LSI方法的定题邮件类信息过滤系统,该系统采用潜在语义模型来表示被过滤的信息类,通过奇异值分解和正例监护学习方法,选择支持向量机(SVM)来识别和分类预定义的定题信息。实验结果表明:基于LSI的特征选择的SVM分类算法是一种更有效的信息识别和文本分类方法,不但具有较好的分类性能,同时也能大大减小计算的复杂性。     

Concept Formation During Interactive Theory Revision

This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on aggregation constraints that rely on structural or correlational properties of the concepts themselves, we argue that in an integrated learning system, other learning activities can provide an additional context that focuses concept formation before structural criteria are applied. In particular, we present the concept formation method realized by the KRT and CLT components of the integrated learning system MOBAL. In MOBAL, a concept formation attempt is triggered whenever no existing concept can adequately capture the rule instance and exception sets as they arise from the theory revision activities of the system. We describe how the so-proposed aggregate is characterized by a set of (function-free) first-order Horn clauses and how these are evaluated according to structural criteria to decide about the introduction of the concept into the representation. We show how a structural criterion can be used to ensure that any new concept improves the structure of the knowledge base, and we empirically evaluate how the introduction of new concepts according to different criteria affects the classification accuracy of learned rules.     

基于组块理论的交互式课件设计与教学

阐述了组块理论在CPU结构及执行指令过程教学中的应用,分析了应用组块理论使用FLASH软件制作交互式课件的步骤及进行计算机辅助教学的过程。     

Interactive Semantic Alignment Model: Social Influence and Local Transmission Bottleneck

We provide a computational model of semantic alignment among communicating agents constrained by social and cognitive pressures. We use our model to analyze the effects of social stratification and a local transmission bottleneck on the coordination of meaning in isolated dyads. The analysis suggests that the traditional approach to learning—understood as inferring prescribed meaning from observations—can be viewed as a special case of semantic alignment, manifesting itself in the behaviour of socially imbalanced dyads put under mild pressure of a local transmission bottleneck. Other parametrizations of the model yield different long-term effects, including lack of convergence or convergence on simple meanings only.