Exploiting remote learners in Internet environment with agents
Exploiting remote learners in Internet environment with agents作者机构：National Key Laboratory for Novel Software Technology Nanjing University Nanjing China
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2010年第53卷第1期
页 面：64-76页
核心收录：
学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China (Grant Nos. 60635030,60721002) Jiangsu Science Foundation (Grant No. BK2008018)
主 题：machine learning data mining aggregative-learning mobile agent Internet environment
摘 要：Data in the Internet are scattered on different sites indeliberately, and accumulated and updated frequently but not synchronously. It is infeasible to collect all the data together to train a global learner for prediction; even exchanging learners trained on different sites is costly. In this paper, aggregative-learning is proposed. In this paradigm, every site maintains a local learner trained from its own data. Upon receiving a request for prediction, an aggregative-learner at a local site activates and sends out many mobile agents taking the request to potential remote learners. The prediction of the aggregative-learner is made by combining the local prediction and the responses brought back by the agents. Theoretical analysis and simulation experiments show the superiority of the proposed method.
同方期刊数据库