Blog
About

  • Record: found
  • Abstract: not found
  • Article: not found

Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

Read this article at

ScienceOpenPublisher
Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      Related collections

      Most cited references 32

      • Record: found
      • Abstract: not found
      • Article: not found

      Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

        Bookmark
        • Record: found
        • Abstract: not found
        • Article: not found

        A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

          Bookmark
          • Record: found
          • Abstract: found
          • Article: not found

          Incremental Support Vector Learning for Ordinal Regression.

          Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR. Recently, an interesting accurate on-line algorithm was proposed for training ν -support vector classification (ν-SVC), which can handle a quadratic formulation with a pair of equality constraints. In this paper, we first present a modified SVOR formulation based on a sum-of-margins strategy. The formulation has multiple constraints, and each constraint includes a mixture of an equality and an inequality. Then, we extend the accurate on-line ν-SVC algorithm to the modified formulation, and propose an effective incremental SVOR algorithm. The algorithm can handle a quadratic formulation with multiple constraints, where each constraint is constituted of an equality and an inequality. More importantly, it tackles the conflicts between the equality and inequality constraints. We also provide the finite convergence analysis for the algorithm. Numerical experiments on the several benchmark and real-world data sets show that the incremental algorithm can converge to the optimal solution in a finite number of steps, and is faster than the existing batch and incremental SVOR algorithms. Meanwhile, the modified formulation has better accuracy than the existing incremental SVOR algorithm, and is as accurate as the sum-of-margins based formulation of Shashua and Levin.
            Bookmark

            Author and article information

            Journal
            IEEE Access
            IEEE Access
            Institute of Electrical and Electronics Engineers (IEEE)
            2169-3536
            2016
            2016
            : 4
            :
            : 5896-5907
            10.1109/ACCESS.2016.2597169
            © 2016
            Product

            Comments

            Comment on this article