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Open AccessArticle

An Approach to Hyperparameter Optimization for the Objective Function in Machine Learning

Department of Computer Engineering, Pukyong National University, Pusan 48513, Korea
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Electronics 2019, 8(11), 1267; https://doi.org/10.3390/electronics8111267
Received: 24 September 2019 / Revised: 20 October 2019 / Accepted: 26 October 2019 / Published: 1 November 2019
In machine learning, performance is of great value. However, each learning process requires much time and effort in setting each parameter. The critical problem in machine learning is determining the hyperparameters, such as the learning rate, mini-batch size, and regularization coefficient. In particular, we focus on the learning rate, which is directly related to learning efficiency and performance. Bayesian optimization using a Gaussian Process is common for this purpose. In this paper, based on Bayesian optimization, we attempt to optimize the hyperparameters automatically by utilizing a Gamma distribution, instead of a Gaussian distribution, to improve the training performance of predicting image discrimination. As a result, our proposed method proves to be more reasonable and efficient in the estimation of learning rate when training the data, and can be useful in machine learning. View Full-Text
Keywords: bayesian optimization; gaussian process; learning rate; acauisition function; machine learning bayesian optimization; gaussian process; learning rate; acauisition function; machine learning
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Kim, Y.; Chung, M. An Approach to Hyperparameter Optimization for the Objective Function in Machine Learning. Electronics 2019, 8, 1267.

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