论文标题
粒子群优化的高参数估计方法
Hyper-parameter estimation method with particle swarm optimization
论文作者
论文摘要
粒子群优化（PSO）方法不能直接用于高参数估计问题，因为尚不清楚从高参数到损失函数或概括精度的数学公式。贝叶斯优化（BO）框架能够将超参数的优化转换为采集函数的优化。采集函数是非凸和多峰。因此，PSO可以更好地解决问题。本文中提出的方法使用粒子群方法来优化BO框架中的采集功能，以获得更好的超参数。评估和证明了分类模型和回归模型中提出的方法的性能。几个基准问题的结果得到了改善。
Particle swarm optimization (PSO) method cannot be directly used in the problem of hyper-parameter estimation since the mathematical formulation of the mapping from hyper-parameters to loss function or generalization accuracy is unclear. Bayesian optimization (BO) framework is capable of converting the optimization of the hyper-parameters into the optimization of an acquisition function. The acquisition function is non-convex and multi-peak. So the problem can be better solved by the PSO. The proposed method in this paper uses the particle swarm method to optimize the acquisition function in the BO framework to get better hyper-parameters. The performances of proposed method in both of the classification and regression models are evaluated and demonstrated. The results on several benchmark problems are improved.
