一种基于DOM的Web文档主题划分方法

主题划分是多主题文档自动摘要中的一个重要问题,提出了一种以网页结构为指导,利用页面对应DOM树中节点的自然分割功能以及相邻边界节点语义相似度的比较进行网页主题划分的方法.实验结果表明该方法具有较高的划分准确率,在此基础上抽取的网页摘要可显著增加文摘内容对原文的覆盖率、有效解决Web文档摘要分布不平衡问题.     

一种改进WebGIS实时监控的通用框架

针对传统WebGIS实时监控应用中面临的主要问题,引入AJAX和SVG技术进行改进,并对这两种技术在WebGIS实时监控中的应用及其优点进行了分析,最后给出了一种通用框架模型.     

一种基于数据连续性的聚类方法

为了解决数据遗漏问题,根据类内样品点均匀分布的性质提出了一种新的聚类方法,即：先从样品集中获得蕴含式类,然后通过支持度获得标准类,最后通过隶属度标识类内成员的强弱程度。该方法仅对类内样品点均匀分布性敏感,不需要预先设置包括类核坐标、类间距离阈值等先验参数,对非凸性类也能有效分类。同时,实验表明该方法是一个具有较高性能的聚类方法。     

基于织物结构特征和小波变换的快速疵点检测

给出了一种利用小波为分析工具,基于织物采样图像进行疵点检测的新方法.首先对织物疵点图片进行了奇偶行列采样抽取,得到奇偶两幅子图像并使得图像的总数据变为原来的二分之一,然后再进行小波变换.在进行小波变换时,采用邻域插值,使得紧支撑小波滤波器的系数序列得到缩短,并可以由原来的滤波器系数计算出缩短后的滤波器系数.实验结果表明该方法可以在保证检测效果准确性的同时,大幅减少数据计算量、提升检测速度.     

一种基于叶分量分析的带有监督信息的在线学习方法

在一些模式识别应用中,具有类属信息的样本数量较少,此时监督学习算法会遇到小样本问题,导致分类器的识别精度大幅低于预期水平.基于叶分量分析,提出一种带监督信息的在线学习方法.该方法在训练过程进行监督学习,而在模式识别阶段能够在对输入样本进行分类的同时基于这些样本进行非监督在线学习,因此实现了监督学习与非监督学习的结合.在小本量情况下,在线学习可以弥补训练阶段监督学习的不足,仍能保证获得较高的识别精度.实验证明,该方法能够有效克服小样本问题.     

帧间差分与边缘纹理相融合的运动目标检测

提出了一种利用边缘纹理进行背景建模,结合三帧差分算法,获取目标轮廓的运动目标检测方法,进一步通过区域填充得到目标前景.该方法对光线变化不敏感,对阴影去除有比较好的效果,改进了边缘纹理差分和帧间差分的缺陷,取得了比较完整准确的运动目标前景.     

公共卫生信息平台软件工程化开发探讨

本文讲述了在公共卫生信息平台项目的开发过程中采用软件工程化方法和技术进行平台的开发。分析了软件工程化开发现状及需求,探讨了软件工程化目标和软件工程化技术路线,并给出了软件工程化开发步骤。     

基于信令降频码方法的信令寻径式光纤交换机的研究

信令寻径式高速光纤交换机需要使用大量"串/并"转换器件提取信令来实现对信道的控制,这使得交换机的底板面积十分庞大,无法检测BGA封装焊接质量;针对以上问题,提出了一种使用特殊的降频码对信令进行编码,成倍地降低信令的频率,采用普通的FPGA器件即可提取信令,取代了大量占线的"串/并"转换器件,减少了PCB面积和降低了成本;并在此基础上给出了高速光纤交换机的解决方案;目前,基于信令降频码方法的信令寻径式光纤交换机已经调试成功,性能稳定,运行状态良好。     

Predictive Ensemble Pruning by Expectation Propagation

An ensemble is a group of learners that work together as a committee to solve a problem. The existing ensemble learning algorithms often generate unnecessarily large ensembles, which consume extra computational resource and may degrade the generalization performance. Ensemble pruning algorithms aim to find a good subset of ensemble members to constitute a small ensemble, which saves the computational resource and performs as well as, or better than, the unpruned ensemble. This paper introduces a probabilistic ensemble pruning algorithm by choosing a set of “sparse” combination weights, most of which are zeros, to prune the ensemble. In order to obtain the set of sparse combination weights and satisfy the nonnegative constraint of the combination weights, a left-truncated, nonnegative, Gaussian prior is adopted over every combination weight. Expectation propagation (EP) algorithm is employed to approximate the posterior estimation of the weight vector. The leave-one-out (LOO) error can be obtained as a by-product in the training of EP without extra computation and is a good indication for the generalization error. Therefore, the LOO error is used together with the Bayesian evidence for model selection in this algorithm. An empirical study on several regression and classification benchmark data sets shows that our algorithm utilizes far less component learners but performs as well as, or better than, the unpruned ensemble. Our results are very competitive compared with other ensemble pruning algorithms.     

Language Structure Using Fuzzy Similarity

Learning of (context-free) grammar rules that are based on alignment between texts of a given collection of sentences has attracted the attention of many researchers. We define and study the alignment profile and formulate fuzzy similarity of alignment profiles for a given collection of sentences. Using the fuzzy-similarity-based profile alignment, we give a methodology to formulate stochastic context-free grammar (CFG) rules. We introduce profile-alignment-based dynamic sentence similarity threshold to formulate the rules of stochastic CFG. The proposed methodology is tested using Child Language Data Exchange System (CHILDES) dataset of sentences. The benefits of our approach are experimentally demonstrated. Since our approach does not make use of any domain knowledge, it is expected to be useful in wide variety of applications requiring model construction.