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Algorithms for Hyper-Parameter Optimization
International audienceSeveral recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel ap- proaches to feature learning. Traditionally, hyper-parameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it pos- sible to run more trials and we show that algorithmic approaches can find better results. We present hyper-parameter optimization results on tasks of training neu- ral networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the ex- pected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreli- able for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [1] and find significantly better results than the best previously reported. This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice
Machine learning algorithms have been used widely in various applications and
areas. To fit a machine learning model into different problems, its
hyper-parameters must be tuned. Selecting the best hyper-parameter
configuration for machine learning models has a direct impact on the model's
performance. It often requires deep knowledge of machine learning algorithms
and appropriate hyper-parameter optimization techniques. Although several
automatic optimization techniques exist, they have different strengths and
drawbacks when applied to different types of problems. In this paper,
optimizing the hyper-parameters of common machine learning models is studied.
We introduce several state-of-the-art optimization techniques and discuss how
to apply them to machine learning algorithms. Many available libraries and
frameworks developed for hyper-parameter optimization problems are provided,
and some open challenges of hyper-parameter optimization research are also
discussed in this paper. Moreover, experiments are conducted on benchmark
datasets to compare the performance of different optimization methods and
provide practical examples of hyper-parameter optimization. This survey paper
will help industrial users, data analysts, and researchers to better develop
machine learning models by identifying the proper hyper-parameter
configurations effectively.Comment: 69 Pages, 10 tables, accepted in Neurocomputing, Elsevier. Github
link:
https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithm
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