Speeding up Common Hyperparameter Optimization Methods by a Two-Phase-Search

被引：0

作者：

Wendt, Alexander ^{[1
]}

Wuschnig, Marco ^{[1
]}

Lechner, Martin ^{[1
]}

机构：

[1] TU Vienna, ICT, Christian Doppler Lab Embedded Machine Learning, Vienna, Austria

来源：

IECON 2020: THE 46TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2020年

关键词：

hyperparameter; machine learning; support vector machine; random forest; Bayesian optimization; optimization;

D O I：

10.1109/iecon43393.2020.9254801

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Hyperparameter search concerns everybody who works with machine learning. We compare publicly available hyperparameter searches on four datasets. We develop metrics to measure the performance of hyperparameter searches across datasets of different sizes as well as machine learning algorithms. Further, we propose a method of speeding up the search by using subsets of data. Results show that random search performs well compared to Bayesian methods and that a combined search can speed up the search by a factor of 5.

引用

页码：517 / 522

页数：6

共 44 条

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