Learning with coefficient-based regularization has attracted a considerable amount of attention in recent years, on both theoretical analysis and applications. In this paper, we study coefficient-based learning scheme...Learning with coefficient-based regularization has attracted a considerable amount of attention in recent years, on both theoretical analysis and applications. In this paper, we study coefficient-based learning scheme （CBLS） for regression problem with /q-regularizer （1 〈 q ≤ 2）. Our analysis is conducted under more general conditions, and particularly the kernel function is not necessarily positive definite. This paper applies concentration inequality with/2-empirical covering numbers to present an elaborate capacity dependence analysis for CBLS, which yields sharper estimates than existing bounds. Moreover, we estimate the regularization error to support our assumptions in error analysis, also provide an illustrative example to further verify the theoretical results.展开更多
Virtual Machines are the core of cloud computing and are utilized toget the benefits of cloud computing. Other essential features include portability,recovery after failure, and, most importantly, creating the core me...Virtual Machines are the core of cloud computing and are utilized toget the benefits of cloud computing. Other essential features include portability,recovery after failure, and, most importantly, creating the core mechanismfor load balancing. Several study results have been reported in enhancing loadbalancingsystems employing stochastic or biogenetic optimization methods.It examines the underlying issues with load balancing and the limitationsof present load balance genetic optimization approaches. They are criticizedfor using higher-order probability distributions, more complicated solutionsearch spaces, and adding factors to improve decision-making skills. Thus, thispaper explores the possibility of summarizing load characteristics. Second,this study offers an improved prediction technique for pheromone level predictionover other typical genetic optimization methods during load balancing.It also uses web-based third-party cloud service providers to test and validatethe principles provided in this study. It also reduces VM migrations, timecomplexity, and service level agreements compared to other parallel standardapproaches.展开更多
基金supported by National Natural Science Foundation of China (Grant Nos.11226111 and 71171166)
文摘Learning with coefficient-based regularization has attracted a considerable amount of attention in recent years, on both theoretical analysis and applications. In this paper, we study coefficient-based learning scheme （CBLS） for regression problem with /q-regularizer （1 〈 q ≤ 2）. Our analysis is conducted under more general conditions, and particularly the kernel function is not necessarily positive definite. This paper applies concentration inequality with/2-empirical covering numbers to present an elaborate capacity dependence analysis for CBLS, which yields sharper estimates than existing bounds. Moreover, we estimate the regularization error to support our assumptions in error analysis, also provide an illustrative example to further verify the theoretical results.
文摘Virtual Machines are the core of cloud computing and are utilized toget the benefits of cloud computing. Other essential features include portability,recovery after failure, and, most importantly, creating the core mechanismfor load balancing. Several study results have been reported in enhancing loadbalancingsystems employing stochastic or biogenetic optimization methods.It examines the underlying issues with load balancing and the limitationsof present load balance genetic optimization approaches. They are criticizedfor using higher-order probability distributions, more complicated solutionsearch spaces, and adding factors to improve decision-making skills. Thus, thispaper explores the possibility of summarizing load characteristics. Second,this study offers an improved prediction technique for pheromone level predictionover other typical genetic optimization methods during load balancing.It also uses web-based third-party cloud service providers to test and validatethe principles provided in this study. It also reduces VM migrations, timecomplexity, and service level agreements compared to other parallel standardapproaches.
