计算机工程2009，Vol.35Issue(22)：191-193,3.
基于蚁群优化的DBN转移网络结构学习算法
Structure Learning Algorithm for DBN Transition Networks Based on Ant Colony Optimization
摘要
Abstract
Aiming at the characteristics of dynamic Bayesian transition networks, this paper proposes a structure learning algorithm based on Ant Colony Optimization(ACO) named ACO-DBN-2S by extending the static Bayesian networks structure leaning algorithm I-ACO-B. In ACO-DBN-2S, ants select arcs from the inter-ares between time slices before from the intra-arcs in one slice, and the interval optimization strategy is improved by decreasing the times of optimizatien operation. A number of experiments under standard datasets demonstrate the algorithm can handle large data, and the precision and speed of learning are improved.关键词
动态贝叶斯网络/转移网络/结构学习/蚁群优化Key words
Dynamic Bayesian Networks(DBN)/transition networks/structure learning/Ant Colony Optimization(ACO)分类
信息技术与安全科学引用本文复制引用
胡仁兵,翼俊忠,张鸿勋,刘椿年..基于蚁群优化的DBN转移网络结构学习算法[J].计算机工程,2009,35(22):191-193,3.基金项目
国家自然科学基金资助重大项目(60496322) （60496322）
北京市教育委员会科技发展基金资助项目(KM200610005020) （KM200610005020）
