Learning Bayesian network is an NP-hard problem. When the number of variables is large, the process of searching optimal network structure could be very time consuming and tends to return a structure which is local op...Learning Bayesian network is an NP-hard problem. When the number of variables is large, the process of searching optimal network structure could be very time consuming and tends to return a structure which is local optimal.The particle swarm optimization (PSO) was introduced to the problem of learning Bayesian networks and a novel structure learning algorithm using PSO was proposed. To search in directed acyclic graphs spaces efficiently, a discrete PSO algorithm especially for structure learning was proposed based on the characteristics of Bayesian networks. The results of experiments show that our PSO based algorithm is fast for convergence and can obtain better structures compared with genetic algorithm based algorithms.展开更多
By importing the idea of P2P,and transmitting messages among clients directly into the client-server architecture,a new hybrid architecture was presented with the help of AOI technology and message category.Theoretica...By importing the idea of P2P,and transmitting messages among clients directly into the client-server architecture,a new hybrid architecture was presented with the help of AOI technology and message category.Theoretical analysis of this architecture was presented in detail.A series of simulation experiments was carried out to verify its effectiveness.Results indicate that the new architecture produces less server message workload than traditional architectures,which can improve the scalability of DVE systems.展开更多
基金National Natural Science Foundation of Chi-na (No.60374071)Zhenjiang Commissionof Science and Technology ( No.2003C11009)
文摘Learning Bayesian network is an NP-hard problem. When the number of variables is large, the process of searching optimal network structure could be very time consuming and tends to return a structure which is local optimal.The particle swarm optimization (PSO) was introduced to the problem of learning Bayesian networks and a novel structure learning algorithm using PSO was proposed. To search in directed acyclic graphs spaces efficiently, a discrete PSO algorithm especially for structure learning was proposed based on the characteristics of Bayesian networks. The results of experiments show that our PSO based algorithm is fast for convergence and can obtain better structures compared with genetic algorithm based algorithms.
基金Sponsored by the National Basic Research Program of China(Grant No.2002CB312200)the National Natural Science Foundation of China(Grant No.60575036)
文摘By importing the idea of P2P,and transmitting messages among clients directly into the client-server architecture,a new hybrid architecture was presented with the help of AOI technology and message category.Theoretical analysis of this architecture was presented in detail.A series of simulation experiments was carried out to verify its effectiveness.Results indicate that the new architecture produces less server message workload than traditional architectures,which can improve the scalability of DVE systems.
