An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.     

Factors influencing feature usage in work-related communication

Two studies are presented. The first aimed to identify possible barriers to the uptake and use of commonly available telephony features and to determine whether greater knowledge of features/access codes and availability of user manuals could increase feature usage under certain conditions. Results showed that feature-usage patterns were not affected by any of the manipulations, but that they were determined by specific job demands. Using Constantine and Lockwood's (1999) conceptualization of user roles, a method to support feature bundling decisions for specific target markets was developed and tested in the second study. The method - Strategic User Needs Analysis (SUNA) - was shown to yield a useful balance between high- and low-level information about selected roles. SUNA provided sufficient information to distinguish between PDA feature usage patterns of two similar target user groups as well as to suggest additional features each of the two target groups would find useful. The development of SUNA and observational findings of actual PDA usage are reported.     

MALL在中国的现状及其建筑设计要点

该文介绍了Mall的产生条件和国内发展概况，并结合实例分析，着重论述建筑设计方面的有关问题。     

固定背景下的随机波形提取模型

分析了当前对于从固定背景下的特征符号与背景符号相粘连的图表中提取信息所存在的问题，设计了适用于此类问题的方法模型。提出了一种在固定背景下通过背景符号检测和背景符号擦除来实现随机波形提取的方法。     

指纹细节特征提取与剪枝

指纹图像经预处理后，由于各种原因，会产生数以百计的特征。为了图像匹配的精确和快速，提出一套在细化后的指纹点线图上提取细节特征并对其剪枝的方法，并根据细节特征的拓扑特性，设计了验证其可靠性的算法。通过实验，证明了该后处理过程在去除指纹图像伪特征方法面具有良好的效果。     

A Survey of Inverse Surface Design From Light Transport Behavior Specification

Inverse surface design problems from light transport behavior specification usually represent extremely complex and costly processes, but their importance is well known. In particular, they are very interesting for lighting and luminaire design, in which it is usually difficult to test design decisions on a physical model in order to avoid costly mistakes. In this survey, we present the main ideas behind these kinds of problems, characterize them, and summarize existing work in the area, revealing problems that remain open and possible areas of further research.     

高校电视台节目漫谈

结合高校校园电视台的职能和特点 ,深刻剖析了高校电视台几种类型节目的特点以及对高校校园文化的作用。     

信息融合的飞机识别方法

以信息融合的理论为基础，利用从可见光图像序列和8-12μm长波红外图像序列中提取的信息对不同种类飞机进行识别。采用矩特征并结合BP神经网络的方法，分别在特征级和决策级两个不同层次上实现了信息融合。实验结果表明，通过信息融合进行飞机识别的准确率可达到90％以上。     

玻璃钢管道的特点,设计原理及选用时的注意事项

本文旨在通过对玻璃钢管道及其标准的发展历史、产品特点、基本设计原理、质量影响因素及选用时应注意的事项等方面的介绍，使用户与工程设计人员对该产品有一个比较全面的了解，以便更好地选用并受益于这种管材。     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.