Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorith...Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.展开更多
A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while th...A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not. Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.展开更多
为了挖掘多模态信息潜在的同构语义关系,并学习更好的项目表示,提出一种语义图增强多模态推荐(SGEMR)算法。首先,利用辅助的多模态信息补充历史的用户-项目交互,捕捉用户在不同模态下的偏好;然后,基于度量学习将松散的项目序列重新构建...为了挖掘多模态信息潜在的同构语义关系,并学习更好的项目表示,提出一种语义图增强多模态推荐(SGEMR)算法。首先,利用辅助的多模态信息补充历史的用户-项目交互,捕捉用户在不同模态下的偏好;然后,基于度量学习将松散的项目序列重新构建为紧密的项目-项目语义图,并设计一个语义层级注意力机制,融合项目的多模态信息;同时,提出一个图重构损失函数,使项目表示保留更多的语义关系,从而提高推荐性能。实验结果表明,在3个真实的数据集上与最优基线算法FREEDOM(FREEzes the item-item graph and DenOises the user-item interaction graph simultaneously for Multimodal recommendation)相比,所提算法的Recall@10分别提升了6.70%、11.30%、5.09%,NDCG@10分别提升了9.09%、12.73%、7.62%,并通过多个消融实验,验证了所提算法的有效性。展开更多
How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue.In this paper,four different causal constraints algorithms are added into score calculations to prune possible p...How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue.In this paper,four different causal constraints algorithms are added into score calculations to prune possible parent sets,improving state-ofthe-art learning algorithms’efficiency.Experimental results indicate that exact learning algorithms can significantly improve the efficiency with only a slight loss of accuracy.Under causal constraints,these exact learning algorithms can prune about 70%possible parent sets and reduce about 60%running time while only losing no more than 2%accuracy on average.Additionally,with sufficient samples,exact learning algorithms with causal constraints can also obtain the optimal network.In general,adding max-min parents and children constraints has better results in terms of efficiency and accuracy among these four causal constraints algorithms.展开更多
基金supported by the National Natural Science Foundation of China(7110111671271170)+1 种基金the Program for New Century Excellent Talents in University(NCET-13-0475)the Basic Research Foundation of NPU(JC20120228)
文摘Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.
基金This project was supported by the National Natural Science Foundation of China (70572045).
文摘A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not. Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.
文摘为了挖掘多模态信息潜在的同构语义关系,并学习更好的项目表示,提出一种语义图增强多模态推荐(SGEMR)算法。首先,利用辅助的多模态信息补充历史的用户-项目交互,捕捉用户在不同模态下的偏好;然后,基于度量学习将松散的项目序列重新构建为紧密的项目-项目语义图,并设计一个语义层级注意力机制,融合项目的多模态信息;同时,提出一个图重构损失函数,使项目表示保留更多的语义关系,从而提高推荐性能。实验结果表明,在3个真实的数据集上与最优基线算法FREEDOM(FREEzes the item-item graph and DenOises the user-item interaction graph simultaneously for Multimodal recommendation)相比,所提算法的Recall@10分别提升了6.70%、11.30%、5.09%,NDCG@10分别提升了9.09%、12.73%、7.62%,并通过多个消融实验,验证了所提算法的有效性。
基金supported by the National Natural Science Foundation of China(61573285).
文摘How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue.In this paper,four different causal constraints algorithms are added into score calculations to prune possible parent sets,improving state-ofthe-art learning algorithms’efficiency.Experimental results indicate that exact learning algorithms can significantly improve the efficiency with only a slight loss of accuracy.Under causal constraints,these exact learning algorithms can prune about 70%possible parent sets and reduce about 60%running time while only losing no more than 2%accuracy on average.Additionally,with sufficient samples,exact learning algorithms with causal constraints can also obtain the optimal network.In general,adding max-min parents and children constraints has better results in terms of efficiency and accuracy among these four causal constraints algorithms.
