一种针对不完全观测数据的消费者聚类方法

现有聚类方法都是基于消费者全部的行为信息,对于观测不完全的信息,提出了三阶段聚类方法。首先,使用样本数据的全部信息对消费者聚类;接着仅使用人口统计变量建立分类模型;最后对上述结果进行修正。三阶段聚类方法最大优点是可以将没有入选样本的个体分配到由样本个体得到的行为集群中去,将这个方法应用于电视行业,得到了很有实际应有价值的结果。     

支持向量机在人岗匹配度测算中的应用

在某市住房公积金管理中心的人力资源配置过程中,通过实例研究发现,采用支持向量机的人岗匹配度测算模型相对于BP人工神经网络等其他常规模型匹配精度更高,在样本数量较少的情况下其优越性更加突出。组织在采用支持向量机模型和模糊综合评价法进行人岗匹配度评价时,先应通过科学的工作分析,建立合适的测算指标体系,然后构建岗位匹配度矩阵和岗位候选人模糊矩阵,最后运用最小二乘支持向量机测算匹配度。     

艺术屏风文化语言探析

屏风文化在中国古典装饰艺术领域中有着独特的文化艺术价值,自春秋时期至今经过上千年的演变,屏风的审美语言和实用功能语言都发生了很多变化。目前,屏风文化的研究往往集中在家居装饰领域,对于这一文化中的艺术语言方面没有进行深入细致的分析和探讨。文章通过回顾屏风文化的发展脉络、传统制作工艺技术和材料种类等方面,从深层次的角度探析这一文化独特的艺术语言。     

门限效应、能源消费与中国的经济增长

本文利用非线性门限向量误差修正模型,分析中国1953年到2008年的能源消费与经济增长,并与向量误差模型进行了对比研究.研究结果表明:中国的能源消费与经济增长之间存在着门限效应;利用非线性模型的拟合结果优于线性模型的拟合结果;当误差项低于门限值时,存在长期的双向格兰杰因果关系,实证结果支持反馈假说理论,而当误差项高于门限值时,这种长期双向因果关系消失,二者之间不存在格兰杰因果关系,实证结果支持中性假说理论.因此,本文的重要政策启示是应该动态的看待能源消费与经济增长之间的关系.     

Fertility and women's employment reconsidered: A macro-level time-series analysis for developed countries, 1960–2000

This paper examines causality and parameter instability in the long-run relationship between fertility and women's employment. This is done by a cross-national comparison of macro-level time-series data from 1960 to 2000 for France, West Germany, Italy, Sweden, the UK, and the USA. By applying vector error correction models (a combination of Granger-causality tests with recent econometric time-series techniques) we find causality in both directions. This finding is consistent with simultaneous movements of both variables brought about by common exogenous factors such as social norms, social institutions, financial incentives, and the availability and acceptability of contraception. We find a negative and significant correlation until about the mid-1970s and an insignificant or weaker negative correlation afterwards. This result is consistent with a recent hypothesis in the demographic literature according to which changes in the institutional context, such as changes in childcare availability and attitudes towards working mothers, might have reduced the incompatibility between child-rearing and the employment of women.     

Structural vector autoregressive analysis for cointegrated variables

Summary Vector autoregressive (VAR) models are capable of capturing the dynamic structure of many time series variables. Impulse response functions are typically used to investigate the relationships between the variables included in such models. In this context the relevant impulses or innovations or shocks to be traced out in an impulse response analysis have to be specified by imposing appropriate identifying restrictions. Taking into account the cointegration structure of the variables offers interesting possibilities for imposing identifying restrictions. Therefore VAR models which explicitly take into account the cointegration structure of the variables, so-called vector error correction models, are considered. Specification, estimation and validation of reduced form vector error correction models is briefly outlined and imposing structural short- and long-run restrictions within these models is discussed. I thank an anonymous reader for comments on an earlier draft of this paper that helped me to improve the exposition.     

变频技术的发展及研究方向

介绍了变频技术的发展、分类和应用现状，着重叙述了矩阵式变换器技术的原理以及控制方式，并分析了变频技术的研究方向．     

基于RST和SVM的入侵检测系统

结合粗糙集理论和支持向量机的特点,构建了基于粗糙集理论和支持向量机的知识简化系统,针对入侵检测系统进行了研究分析,建立了基于粗糙集理论和支持向量机的入侵检测系统,最后通过仿真实验比较,其结果表明该系统有效改进了以往的入侵检测系统的。     

Bayesian classification of tumours by using gene expression data

Summary.  Precise classification of tumours is critical for the diagnosis and treatment of cancer. Diagnostic pathology has traditionally relied on macroscopic and microscopic histology and tumour morphology as the basis for the classification of tumours. Current classification frameworks, however, cannot discriminate between tumours with similar histopathologic features, which vary in clinical course and in response to treatment. In recent years, there has been a move towards the use of complementary deoxyribonucleic acid microarrays for the classi-fication of tumours. These high throughput assays provide relative messenger ribonucleic acid expression measurements simultaneously for thousands of genes. A key statistical task is to perform classification via different expression patterns. Gene expression profiles may offer more information than classical morphology and may provide an alternative to classical tumour diagnosis schemes. The paper considers several Bayesian classification methods based on reproducing kernel Hilbert spaces for the analysis of microarray data. We consider the logistic likelihood as well as likelihoods related to support vector machine models. It is shown through simulation and examples that support vector machine models with multiple shrinkage parameters produce fewer misclassification errors than several existing classical methods as well as Bayesian methods based on the logistic likelihood or those involving only one shrinkage parameter.     

基于Java的面向对象牛病诊治专家系统研究

面向对象的专家系统将知识和推理方法封装在对象内部,能很好的模仿人类思维习惯.本文将基于框架和基于规则的推理方法和知识表示结合封装在对象中,采用Java技术,实现了基于B/S结构的简单、高效的面向对象疾病诊断专家系统.