改进ATPE算法，提升机器学习超参优化效率
Modified Adaptive Tree-Structured Parzen Estimator for Hyperparameter Optimization
- 提出ATPE的多项改进方法，增强搜索能力
- 在多个基准函数上验证，优化效果显著提升
- 适合需要高效调参的机器学习实践者
本文回顾了机器学习模型的超参数优化方法，重点研究自适应树状结构帕森估计器（ATPE）算法。我们提出了若干针对ATPE的改进，并在一系列标准基准函数上评估其有效性。实验结果表明，所提改进在选定基准上显著提升了ATPE的超参数优化性能，这一发现对实际机器学习与优化任务具有重要应用价值。
原文摘要 · Abstract (English)
In this paper, we review hyperparameter optimization methods for machine learning models, with a particular focus on the Adaptive Tree-Structured Parzen Estimator (ATPE) algorithm. We propose several modifications to ATPE and assess their efficacy on a diverse set of standard benchmark functions. Experimental results demonstrate that the proposed modifications significantly improve the effectiveness of ATPE hyperparameter optimization on selected benchmarks, a finding that holds practical relevance for their application in real-world machine learning / optimization tasks.
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