全局最优和局部最优是服务选择的两种策略.现有的全局最优服务选择算法提供端对端约束下最优单解而非可接受的多解,既无法充分体现用户偏好和服务个性,也不利于激励服务提供者优化服务质量.首先,在引入序数效用函数作为局部服务排序的...全局最优和局部最优是服务选择的两种策略.现有的全局最优服务选择算法提供端对端约束下最优单解而非可接受的多解,既无法充分体现用户偏好和服务个性,也不利于激励服务提供者优化服务质量.首先,在引入序数效用函数作为局部服务排序的数值尺度的基础上,提出一种基于多维服务质量的局部最优服务选择模型MLOMSS(Multi-QoS based Local Opti mal Model of Service Selection),为自动选取优质服务提供重要依据.然后,构造客观赋权模式、主观赋权模式和主客观赋权模式来确定各服务质量属性的权重,既体现用户偏好和服务质量的客观性,又有助于快速生成聚合服务链.最后,通过语义Web服务集成平台SEWSIP(Semantic Enable Web Serv-ice Integration Platform)证明MLOMSS模型的有效性和灵活性.展开更多
Model selection strategies have been routinely employed to determine a model for data analysis in statistics, and further study and inference then often proceed as though the selected model were the true model that we...Model selection strategies have been routinely employed to determine a model for data analysis in statistics, and further study and inference then often proceed as though the selected model were the true model that were known a priori. Model averaging approaches, on the other hand, try to combine estimators for a set of candidate models. Specifically, instead of deciding which model is the 'right' one, a model averaging approach suggests to fit a set of candidate models and average over the estimators using data adaptive weights.In this paper we establish a general frequentist model averaging framework that does not set any restrictions on the set of candidate models. It broaden, the scope of the existing methodologies under the frequentist model averaging development. Assuming the data is from an unknown model, we derive the model averaging estimator and study its limiting distributions and related predictions while taking possible modeling biases into account.We propose a set of optimal weights to combine the individual estimators so that the expected mean squared error of the average estimator is minimized. Simulation studies are conducted to compare the performance of the estimator with that of the existing methods. The results show the benefits of the proposed approach over traditional model selection approaches as well as existing model averaging methods.展开更多
文摘全局最优和局部最优是服务选择的两种策略.现有的全局最优服务选择算法提供端对端约束下最优单解而非可接受的多解,既无法充分体现用户偏好和服务个性,也不利于激励服务提供者优化服务质量.首先,在引入序数效用函数作为局部服务排序的数值尺度的基础上,提出一种基于多维服务质量的局部最优服务选择模型MLOMSS(Multi-QoS based Local Opti mal Model of Service Selection),为自动选取优质服务提供重要依据.然后,构造客观赋权模式、主观赋权模式和主客观赋权模式来确定各服务质量属性的权重,既体现用户偏好和服务质量的客观性,又有助于快速生成聚合服务链.最后,通过语义Web服务集成平台SEWSIP(Semantic Enable Web Serv-ice Integration Platform)证明MLOMSS模型的有效性和灵活性.
基金supported by National Science Foundation of USA (Grant Nos.DMS1812048,DMS-1737857,DMS-1513483 and DMS-1418042)National Natural Science Foundation of China (Grant No.11529101)
文摘Model selection strategies have been routinely employed to determine a model for data analysis in statistics, and further study and inference then often proceed as though the selected model were the true model that were known a priori. Model averaging approaches, on the other hand, try to combine estimators for a set of candidate models. Specifically, instead of deciding which model is the 'right' one, a model averaging approach suggests to fit a set of candidate models and average over the estimators using data adaptive weights.In this paper we establish a general frequentist model averaging framework that does not set any restrictions on the set of candidate models. It broaden, the scope of the existing methodologies under the frequentist model averaging development. Assuming the data is from an unknown model, we derive the model averaging estimator and study its limiting distributions and related predictions while taking possible modeling biases into account.We propose a set of optimal weights to combine the individual estimators so that the expected mean squared error of the average estimator is minimized. Simulation studies are conducted to compare the performance of the estimator with that of the existing methods. The results show the benefits of the proposed approach over traditional model selection approaches as well as existing model averaging methods.
