由于传统主动学习方法的计算量随着问题规模的增大呈指数增长,因此很难应用于大规模多类数据分类任务中。为解决该问题,设计了一种基于子抽样的主动学习(subsampling-based active learning,SBAL)算法。该算法将无监督聚类算法与传统主...由于传统主动学习方法的计算量随着问题规模的增大呈指数增长,因此很难应用于大规模多类数据分类任务中。为解决该问题,设计了一种基于子抽样的主动学习(subsampling-based active learning,SBAL)算法。该算法将无监督聚类算法与传统主动学习方法整合,在二者之间增加了子抽样操作,该操作能够显著降低算法的时间复杂度,在保证实验准确率的基础上减少实验耗时,从而更加高效地处理大规模数据集的分类问题。实验结果显示,采用SBAL算法的实验性能优于传统主动学习算法,证明了所提算法可以突破传统主动学习方法不能处理大规模数据集多类别分类问题的局限性。展开更多
The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense ...The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.展开更多
文摘由于传统主动学习方法的计算量随着问题规模的增大呈指数增长,因此很难应用于大规模多类数据分类任务中。为解决该问题,设计了一种基于子抽样的主动学习(subsampling-based active learning,SBAL)算法。该算法将无监督聚类算法与传统主动学习方法整合,在二者之间增加了子抽样操作,该操作能够显著降低算法的时间复杂度,在保证实验准确率的基础上减少实验耗时,从而更加高效地处理大规模数据集的分类问题。实验结果显示,采用SBAL算法的实验性能优于传统主动学习算法,证明了所提算法可以突破传统主动学习方法不能处理大规模数据集多类别分类问题的局限性。
基金supported by the National Natural Science Foundation of China (61903025)the Fundamental Research Funds for the Cent ral Universities (FRF-IDRY-20-013)。
文摘The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.
