A computational study of distributed rule learning

• 成果类型: Article
• 署名作者: Sikora, R; Shaw, MJ
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.7.2.189
• 发表日期: 1996
• 页码: 189-197
• 关键词: Optimization; induction; support

摘要

This report is concerned with a rule learning system called the Distributed Learning System (DLS). Its objective is two-fold: First, as the main contribution, the DLS as a rule-learning technique is described and the resulting computational performance is presented, with definitive computational benefits clearly demonstrated to show the efficacy of using the DLS. Second, the important parameters of the DLS are identified to show the characteristics of the Group Problem Solving (GPS) strategy as implemented in the DLS. On one hand this helps us pinpoint the critical designs of the DLS for effective rule learning; on the other hand this analysis can provide insight into the use of GPS as a more general rule-learning strategy.