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Beyond Manual Tuning of Hyperparameters
Citation key Hutter2015
Author Hutter, F. and Lücke, J. and Schmidt-Thieme, L.
Pages 329-337
Year 2015
ISSN 0933-1875, 1610-1987
DOI 10.1007/s13218-015-0381-0
Journal KI - Künstliche Intelligenz
Volume 29
Number 4
Publisher Springer Berlin Heidelberg
Abstract The success of hand-crafted machine learning systems in many applications raises the question of making machine learning algorithms more autonomous, i.e., to reduce the requirement of expert input to a minimum. We discuss two strategies towards this goal: (1) automated optimization of hyperparameters (including mechanisms for feature selection, preprocessing, model selection, etc) and (2) the development of algorithms with reduced sets of hyperparameters. Since many research directions (e.g., deep learning), show a tendency towards increasingly complex algorithms with more and more hyperparamters, the demand for both of these strategies continuously increases. We review recent hyperparameter optimization methods and discuss data-driven approaches to avoid the introduction of hyperparameters using unsupervised learning. We end in discussing how these complementary strategies can work hand-in-hand, representing a very promising approach towards autonomous machine learning.
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