基于支持向量机的短期GDP预测模型与应用研究

支持向量机是最近几年国际上模式识别研究的热点,具有全局最优和良好的泛化能力.本文在理论分析江门市GDP预测指标体系的前提下,研究了基于SVM的预测方法,并运用实际数据进行建模和预测,获得了比较准确的预测结果.     

MEMS--电子领域的新宠

微机电系统技术(MEMS)是随着半导体集成电路微细加工技术和超精密机械加工技术的发展而发展起来的．其微型化、集成化、批量生产和多学科交叉等优良特性将对人类社会生活带来巨大的进步，是电子领域的新宠。     

不相容决策信息系统属性核的研究

决策表属性核的计算往往是基于Rough集理论的决策信息系统约简过程的出发点和关键．不相容决策信息系统是信息系统约简处理研究的重点．本文对不相容决策信息系统属性核的计算问题进行研究，证明了Rough集理论代数观和信息观中属性核概念的包含关系，以及几种属性核计算方法所得到的结果之间的包含关系．     

通用的机械零件技术参数数据查询引擎 GMDSE的设计与实现

机械零件数据管理是用信息技术改造传统产业的重要内容.这里探讨了机械零件数据管理技术,并以基本零件轴键数据查询为例,阐述了以Delphi中的ADO组件连接加密的MS-ACCESS数据库实现这一目标的技术.     

一种大量程疲劳试验机实现小试样预制裂纹的方法

根据简单的力学原理，提出了一种使用大量程程控高频疲劳试验机，按照国标2038-91标准预制三点弯曲小试样裂纹的方法——双试样法，并与单试样法进行了对比，给出了实验数据，说明了双试样法的优点，详细给出了三点弯曲试样裂纹预制的相关公式及确定各个载荷具体方法。     

电子选针中的数据存储量的分析

在分析选针循环形成机理的基础上，推荐一组减小选针循环的优化参数。对电子选针的数据形成、选针循环的最小数据存储量进行了阐述，探讨了选针循环数据的平行算法和交错算法，为电子提花圆纬机花型软件的开发提供一些理论基础。     

智能设备的原理与开发技术

智能设备是基于计算机网络设备的自助应用系统中的重要组成部分，对改善服务质量、提升企业形象、拓展业务有重要意义。结合开发经历，总结讨论了智能设备的现状、组成原理、应用模式、软件开发、安全及整合等问题。     

An extended Lagrangian support vector machine for classifications

Lagrangian support vector machine (LSVM) cannot solve large problems for nonlinear kernel classifiers. In order to extend the LSVM to solve very large problems, an extended Lagrangian support vector machine (ELSVM) for classifications based on LSVM and SVMlight is presented in this paper. Our idea for the ELSVM is to divide a large quadratic programming problem into a series of subproblems with small size and to solve them via LSVM. Since the LSVM can solve small and medium problems for nonlinear kernel classifiers, the proposed ELSVM can be used to handle large problems very efficiently. Numerical experiments on different types of problems are performed to demonstrate the high efficiency of the ELSVM.     

Induction of hybrid decision tree based on post-discretization strategy

By redefining test selection measure, we propose in this paper a new algorithm, Flexible NBTree, which induces a hybrid of decision tree and Naive Bayes. Flexible NBTree mitigates the negative effect of information loss on test selection by applying postdiscretization strategy: at each internal node in the tree, we first select the test which is the most useful for improving classification accuracy, then apply discretization of continuous tests. The finial decision tree nodes contain univariate splits as regular decision trees, but the leaves contain Naive Bayesian classifiers. To evaluate the performance of Flexible NBTree, we compare it with NBTree and C4.5, both applying pre-discretization of continuous attributes. Experimental results on a variety of natural domains indicate that the classification accuracy of Flexible NBTree is substantially improved.