Zahraa Saddi Kadhim, Hasanen S. Abdullah, K. I. Ghathwan
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引用次数: 3

摘要

机器学习(ML)方法通常用于计算机视觉、推荐系统、自然语言处理(NLP)以及用户行为分析等应用程序。神经网络(Neural Networks, NNs)是ML最基本的方法之一;设计神经网络最具挑战性的因素是确定使用哪些超参数来生成最优模型,其中超参数优化可以提高神经网络的性能。本研究包括对几种类型的神经网络以及一些超参数优化方法的简要解释,以及先前使用优化方法增强神经网络性能的工作结果,这些优化方法通过识别适当的超参数配置来帮助研究人员和数据分析师开发更好的ML模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Neural Network Hyperparameters Optimization: A Survey
Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics. Neural Networks (NNs) are one of the most es-sential ways to ML; the most challenging element of designing a NN is de-termining which hyperparameters to employ to generate the optimal model, in which hyperparameter optimization improves NN performance. This study includes a brief explanation regarding a few types of NN as well as some methods for hyperparameter optimization, as well as previous work results in enhancing ANN performance using optimization methods that aid research-ers and data analysts in developing better ML models via identifying the ap-propriate hyperparameter configurations.
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