论文标题
分层协作超参数调整
Hierarchical Collaborative Hyper-parameter Tuning
论文作者
论文摘要
高参数调整是构建机器学习解决方案中最关键的阶段之一。本文展示了如何利用多代理系统来开发分布式技术，以确定机器学习模型中任何任意的超参数集的近乎最佳值。所提出的方法采用分布式形成的基于层次代理的结构来调整超参数值的合作搜索过程。呈现的通用模型用于开发一种具有指导的基于随机代理的调谐技术，并且在机器学习和全局功能优化应用程序中都研究了其行为。根据经验结果，所提出的模型在分类误差和功能评估方面优于其基本的随机调整策略，特别是在较高的维度中。
Hyper-parameter Tuning is among the most critical stages in building machine learning solutions. This paper demonstrates how multi-agent systems can be utilized to develop a distributed technique for determining near-optimal values for any arbitrary set of hyper-parameters in a machine learning model. The proposed method employs a distributedly formed hierarchical agent-based architecture for the cooperative searching procedure of tuning hyper-parameter values. The presented generic model is used to develop a guided randomized agent-based tuning technique, and its behavior is investigated in both machine learning and global function optimization applications. According the empirical results, the proposed model outperformed both of its underlying randomized tuning strategies in terms of classification error and function evaluations, notably in higher number of dimensions.
