Search engines’ responses to several search feature selections

The authors of this paper investigated the impact of the advanced search features of three common search engines on retrieval result performance: Yahoo, Google, and Live Search. The authors analyzed 240 search queries with different information need emphases to determine retrieval effectiveness differences among regular search, title search, exact phrase search, and PDF file format restriction search. A one-way ANOVA method and regression analysis method were used for the study. It was found that the PDF file format restriction search achieved the best retrieval performance among Yahoo, Google and Live Search. The regular search achieved the best web page ranking performance among Yahoo, Google, and Live Search. The findings of this study can be used to assist users in formulating an appropriate search strategy to improve search effectiveness, and to shed light on how search engines react to different types of search features in terms of retrieval effectiveness.     

基于多项式样条回归的基尼系数的算法

基尼系数的测算目前有分布函数法、几何计算法与曲线拟合法。本文推导出洛伦兹曲线的三次分段多项式样条函数拟合的简单表达式,再通过样条回归确定系数,从而可以比较精确地求出基尼系数,并在此基础上进行了实证分析,且计算结果比文献[6]的结果要精确。     

伊犁新垦区土壤全盐量和电导率定量关系探讨

土壤含盐量和电导率是调查评价土壤盐渍化程度的两种指标,进行两者关系的定量探讨是解决土壤盐渍化快速诊断问题的前提。本文以伊犁新垦区为例,采用传统的一次函数、二次函数和分段函数等对土壤含盐量和电导率关系进行回归拟合,同时应用土地利用类型、植被类型和土壤类型等环境因子作为辅助数据对两者进行分区线性拟合,并分析比较两种方法的优劣。结果表明,随着全盐量的增加,土壤全盐量和电导率呈现一定的非线性关系,以土壤全盐量为阈值的分段函数拟合效果优于一次函数和二次函数,其RMSE小于后两者;以土地利用类型作为辅助数据进行的分组拟合结果优于植被类型和土壤类型的效果,也略优于分段函数的拟合精度。该方法在宏观尺度上为快速、精确获取土壤盐分状况提供帮助。     

An Intuitive Test of Structural Change

This note suggests a simple, intuitive approach to testing for structural changes in time series data. The procedure is illustrated using historical data from a recent issue of Teaching Statistics.     

经济增长促进我国档案发展的实证研究

随着信息技术的进步,我国档案业面临着新的发展趋势。运用因子分析法对我国档案发展现状进行实证研究,结果表明除2005和2012年,国家档案业呈稳固上升发展趋势。回归分析和格兰杰因果关系检验结果显示,我国档案业发展不是推动经济增长的原因,而经济增长是促进我国档案业发展的原因,二者之间存在单向因果关系。从当前我国档案管理中存在的问题入手,提出若干发展建议。     

时序与回归预测方法比较研究

基于神经网络的时序和回归预测方法,对同一数据建立BP、RBF神经网络及小波神经网络(WNN)模型,分别采用两种方法预测,比较实验结果,指出各自的优点和存在的问题。仿真实验表明回归预测法高于时序预测精度,但回归预测法要求一定规模的数据且规律性好,它需提供各影响因子数据才能进行并行递推预测;时序预测一般精度较差,但数据采集方便,其可行性和实用性好,适用于数据信息少的环境等特点。     

A Demonstration of Regression False Positive Selection in Data Mining

Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection (type I errors) is nearly certain to occur. This article describes an in‐class demonstration that shows the frequency and impact of false positives on data mining regression‐based predictive modeling. In this demonstration, 500 randomly generated independent (X) variables are individually regressed against a single, randomly generated (Y) variable, and the resulting 500 p‐values are sorted and examined. This experiment is repeated and the distribution of the number of variables significant at the 5% level resulting from this simulation is presented and discussed. The demonstration provides a tangible example in which students see the reality and risks of incorrectly inferring statistical significance of independent regression variables. Students have expressed a deeper understanding and appreciation of the risks of type I errors through this demonstration. This demonstration is innovative because the scale of the simulation allows the students to experience the near certainty that the correlations shown in the results are truly random.     

Applying regression models to query-focused multi-document summarization

Most existing research on applying machine learning techniques to document summarization explores either classification models or learning-to-rank models. This paper presents our recent study on how to apply a different kind of learning models, namely regression models, to query-focused multi-document summarization. We choose to use Support Vector Regression (SVR) to estimate the importance of a sentence in a document set to be summarized through a set of pre-defined features. In order to learn the regression models, we propose several methods to construct the “pseudo” training data by assigning each sentence with a “nearly true” importance score calculated with the human summaries that have been provided for the corresponding document set. A series of evaluations on the DUC data sets are conducted to examine the efficiency and the robustness of the proposed approaches. When compared with classification models and ranking models, regression models are consistently preferable.     

基于FitNesse的自动化回归测试工具的设计与实现

回归测试是软件测试生命周期中的一个十分重要且成本昂贵的过程。在FitNesse基础上,设计并实现了一种自动化回归测试工具（Automated Regression Testing Tool,以下简称ARTT）,该工具解析被测试系统SUT（SystemUnder Test）,自动生成运行在FitNesse环境下的测试用例框架及测试代码。实验结果表明,该工具对减少回归测试成本,提高回归测试效率具有十分重要的意义。     

城市居民低碳消费行为及影响因素研究——以福建省福州市为例

由城市居民消费产生的碳排放量问题日益突出,并对空气质量及生态环境构成一定的威胁。本文通过对福州市城区的实地问卷调查,运用描述统计、单因素方差模型和多元线性回归模型实证分析了城市居民的低碳消费行为问题。研究结果显示:当前城市居民实施低碳消费的行为倾向处于中等略偏上的水平,反映出调控城市居民低碳消费行为的重要性;人口统计学变量对于城市居民低碳消费行为的影响并无显著差异,反映出低碳消费并没有"专属区",具有明显的社会共性特征;而"价值观"、"关注度"、"行政性政策"、"操作能力"、"社会规范"5个变量对城市居民低碳消费行为产生了显著的正向影响作用,反映出心理因素、法规政策及外部因素等会对低碳消费行为产生影响作用。因此,建议开展全员式的低碳消费理念普及与教育活动,引导城市居民正确认知低碳消费行为对自身、经济发展及生态环境产生的价值,提高城市居民实施低碳消费行为的操作能力,并适时出台促进实施低碳消费行为的相关法规政策,从而努力减少由城市居民消费所产生的碳排放量以推动可持续发展。