论文标题
多任务多准则超参数优化
Multi-Task Multicriteria Hyperparameter Optimization
论文作者
论文摘要
我们提出了一种新方法，用于在几个任务和几个标准之间搜索最佳的超参数。多任务多标准方法（MTMC）提供了几种帕累托最佳解决方案，其中选择一种解决方案，具有给定标准显着性系数。本文始于选择最佳超参数问题的数学表述。然后，描述了解决此问题的MTMC方法的步骤。使用卷积神经网络在图像分类问题上评估所提出的方法。本文为各种标准显着性系数提供了最佳的超参数。
We present a new method for searching optimal hyperparameters among several tasks and several criteria. Multi-Task Multi Criteria method (MTMC) provides several Pareto-optimal solutions, among which one solution is selected with given criteria significance coefficients. The article begins with a mathematical formulation of the problem of choosing optimal hyperparameters. Then, the steps of the MTMC method that solves this problem are described. The proposed method is evaluated on the image classification problem using a convolutional neural network. The article presents optimal hyperparameters for various criteria significance coefficients.
