集智慧之大成的信息系统——Internet

90年代初期中国学者提出了开放的复杂巨系统理论,以及处理开放的复杂巨系统的方法论-从定性到定量的综合集成研讨厅体系.本文首先阐述了综合集成研讨厅体系思想的精要,接着从复杂性的角度出发,剖析与揭示了Internet系统的演化性、涌现性、开放性等本质特性,指出Internet是一个集智慧之大成的信息系统,处理Internet系统及其相关问题的方法论是综合集成研讨厅体系.     

基于多智能体的人机协作智能信息系统

综合集成研讨厅理论体系是作为处理开放的复杂巨系统的有关问题而提出的,其实质 是一个人机协作的智能信息系统.本文主要探索研讨厅的实现途径,给出了基于网络的研讨厅的 层次模型与系统结构,提出了基于内嵌客户/Agent/服务器的请求器-中介器-供应器模型的多 智能体研讨厅的多层分布计算模型,并基于Java的智能信息Agent技术探讨了支持宏观经济决 策的多智能体研讨厅的构架及工作机制.研究表明,基于Internet有关技术与Agent计算技术有 可能实现研讨厅,并由于Agent技术的优势而可能得到较好的系统性能.     

神通广大的计算机

计算机是20世纪人类最伟大的发明之一,它的出现有力地推动了社会的进步与发展,并以其为核心导致信息时代的大跃进,社会的每一个角落都有它的烙印,在各行各业中发挥着巨大的作用,对我们的工作和生活产生了深远的影响。 科学家们在火箭发射、天气预报、勘探等科学计算中几麻袋稿纸的手算工作量,用大型计算机可能几秒钟就完成了,而且准确率相当高。用计算机代替人去     

Customer Activity Sequence Classification for Debt Prevention in Social Security

From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-consuming and the large number of patterns discovered also makes the pattern selection and classifier building very time-consuming. The fact is that, in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which are the most strongly correlated to each target class. In this step, an aggressive strategy is employed to select a small set of sequential patterns. Secondly, pattern pruning and serial coverage test are done on the mined patterns. The patterns that pass the serial test are used to build the sub-classifier at the first level of the final classifier. And thirdly, the training samples that cannot be covered are fed back to the sequential pattern mining stage with updated parameters. This process continues until predefined interestingness measure thresholds are reached, or all samples are covered. The patterns generated in each loop form the sub-classifier at each level of the final classifier. Within this framework, the searching space can be reduced dramatically while a good classification performance is achieved. The proposed algorithm is tested in a real-world business application for debt prevention in social security area. The novel sequence classification algorithm shows the effectiveness and efficiency for predicting debt occurrences based on customer activity sequence data.     

基于Iternet的综合集成研讨厅系统体系结构研究

1前言 信息处理系统正变得越来越开放、复杂与面向网络,其中一个重要特征是人已经成为该系统中的组成部分,我们生活中最具体的人工开放复杂系统的例子是Internet.对这样的开放、复杂巨系统的研究已成为信息与知识系统研究的热点之一.人机结合从定性到定量的综合集成研讨厅体系(简称研讨厅体系)是钱学森院士等于10多年前针对处理开放、复杂巨系统[1～3]提出的方法论,其工程技术方法--综合集成法[4],是处理复杂系统尤其是结构不良的复杂问题的求解方法,实际上就是要按照研讨厅体系思想构建处理具体复杂问题的智能信息系统.