Converting numerical classification into text classification

Consider a supervised learning problem in which examples contain both numerical- and text-valued features. To use traditional feature-vector-based learning methods, one could treat the presence or absence of a word as a Boolean feature and use these binary-valued features together with the numerical features. However, the use of a text-classification system on this is a bit more problematic—in the most straight-forward approach each number would be considered a distinct token and treated as a word. This paper presents an alternative approach for the use of text classification methods for supervised learning problems with numerical-valued features in which the numerical features are converted into bag-of-words features, thereby making them directly usable by text classification methods. We show that even on purely numerical-valued data the results of text classification on the derived text-like representation outperforms the more naive numbers-as-tokens representation and, more importantly, is competitive with mature numerical classification methods such as C4.5, Ripper, and SVM. We further show that on mixed-mode data adding numerical features using our approach can improve performance over not adding those features.     

文本挖掘及其应用

文本挖掘是指利用数据挖掘技术，从大量的文本数据中提取感兴趣的、潜在的有用模式和隐藏的信息。本文详细阐述了文本挖掘的功能、文本挖掘的过程、文本挖掘的应用，并在文章最后介绍了文本挖掘的工具及演示实例。     

视频帧中提取文字区域的算法

提出了一种在视频帧中提取文字区域的算法，该算法基于文字的纹理特征——角点，即文字，特别是汉字，含有丰富的角点。首先，在视频帧中进行角点检测，得到视频帧的角点分布图。第2步是进行角点滤波，即在角点分布图中剔除一些比较分散的，可以认为不是从文字中检测到的角点。第3步对角点分布图中余下的角点聚类形成候选文字区域，采用水平方向和竖直方向的投影对这些区域进行分化。第4步文字区域的最后确认，除去噪声。实验表明该算法计算速度快，提取文字区域准确。     

TTFS:一个倾向性文本过滤系统的设计与实现

以往文本过滤的研究主要集中于主题性过滤，然而随着网络的发展，倾向性文本过滤在网络信息安全方面的作用越来越大。论文阐述了一个倾向性文本过滤系统TTFS(Tenclency Text Filtering System)，能够对具有关于某个主题的特定倾向的文本进行过滤。该系统充分利用了领域知识，采用了语义模式分析等技术，实验表明其查全率和查准率高，速度较快。     

基于中文文本分类的分词方法研究

文本分类有助于用户有选择地阅读和处理海量文本,因此其预备工作分词系统的研究是很有意义的。该文主要提出了一种基于中文文本分类的分词方法,区别于常用的基于字符串匹配等方法,并利用数据库特有的查询技术设计和实现了该分词系统,旨在通过新的分词方法提供更加准确的分词率,同时提高系统实现效率。     

文本知识发现:基于信息抽取的文本挖掘蝌

In the general context of Knowledge Discovery, Knowledge Discovery in Text (KDT), which uses TextMining techniques to extract and induce hidden knowledge from unstructured text data, surges in the data and naturallanguage processing research. KDT is a multi-discipline of Artificial Intelligence, Machine learning, Natural Lan-ing with a stressing on its IE (Information Extraction)-based induction and specific sublanguage fields oriented prac-tices.     

一种新的支持向量机主动学习策略及其在文本分类中的应用

There are two well-known characteristics about text classification.One is that the dimension of the sample space is very high,while the number of examples available usually is very small.The other is that the example vectors are sparse.Meanwhile,we find existing support vector machines active learning approaches are subject to the influence of outliers.Based on these observations,this paper presents a new hybrid active learning approach.In this approach,to select the unlabelled example(s) to query,the learner takes into account both sparseness and high-di-mension characteristics of examples as well as its uncertainty about the examples‘‘ categorization.This way, the active learner needs less labeled examples,but still can get a good generalization performance more quickly than competing methods.Our empirical results indicate that this new approach is effective.     

Turbo C文本方式中文下拉式菜单的设计

本文通过调用Ｔｕｒｂｏ Ｃ提供的ＢＩＯＳ中断函数ｉｎｔ８６（）的子功能，介绍用Ｃ语言设计的文本方式中文下拉式菜单。此菜单具有速度快，模块小，人机界面友好等特性。     

An analysis of COMET and MAGIC using the standard reference model for intelligent multimedia presentation systems

We provide an overview of two multimedia generation systems developed at Columbia University, COMET and MAGIC, and analyze them using the standard reference model for intelligent multimedia presentation systems. COMET (Coordinated Multimedia Explanation Testbed) emphasizes the coordinated generation of text and 3D graphics in a military maintenance and repair documentation domain. It features support for automated determination of what information to present, assignment of information to appropriate media and generation of textual cross-references to the structure and content of generated graphics, including the automated redesign of graphics to make possible better cross-references. MAGIC (Multimedia Abstract Generation for Intensive Care) generates presentations that use temporal media, including speech and animation, to explain the status of hospital patients. It extends the media coordination approach used in COMET to include a multi-stage negotiation process that solves temporal constraints imposed by individual media generators to determine when and for how long actions should occur in each medium.     

Text information extraction in images and video: a survey

Text data present in images and video contain useful information for automatic annotation, indexing, and structuring of images. Extraction of this information involves detection, localization, tracking, extraction, enhancement, and recognition of the text from a given image. However, variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely challenging. While comprehensive surveys of related problems such as face detection, document analysis, and image & video indexing can be found, the problem of text information extraction is not well surveyed. A large number of techniques have been proposed to address this problem, and the purpose of this paper is to classify and review these algorithms, discuss benchmark data and performance evaluation, and to point out promising directions for future research.