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- W2894834596 abstract "One of the crucial steps of preparing a neural network model is the process of tuning its hyperparameters. This process can be time-consuming and hard to be done properly by hand. Tuned hyperparameters allow to obtain high accuracy of classification as well as fast training. In this paper we explore the usage of selected heuristic algorithms based on evolutionary approach: Covariance Matrix Adaptation Evolution Strategy (CMAES), Differential Evolution Strategy (DES) and jSO for the hyperparameter tuning task. Results of Multilayer Perceptron’s (MLP) hyperparameter optimization for a real-life dataset are presented. An improvement in models’ performance is observed through the usage of presented approach." @default.
- W2894834596 created "2018-10-12" @default.
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- W2894834596 date "2018-10-01" @default.
- W2894834596 modified "2023-09-27" @default.
- W2894834596 title "Heuristic hyperparameter optimization for multilayer perceptron with one hidden layer" @default.
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- W2894834596 doi "https://doi.org/10.1117/12.2501569" @default.
- W2894834596 hasPublicationYear "2018" @default.
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