基于深度强化学习的自动特征提取模型研究
Research on Automatic Feature Extraction Model Based on Deep Reinforcement Learning
程凤敏1
作者信息
摘要
介绍了深度强化学习在自动特征提取领域的应用.首先,简单地概述了现有的特征提供取方法,包括传统的特征取方法、基于深度学习的特征提取方法和基于深度强化学习的特征提取方法;其次,阐述了深度强化学习和图像处理技术;最后,详细地介绍了基于深度强化学习的自动特征提取模型的设计方法,包括数据集准备与预处理、模型架构设计、模型训练过程及参数设置、实验结果分析与评估.该模型能够提高图像处理的准确性和效率,具有较高的推广使用价值.
Abstract
The application of deep reinforcement learning in automatic feature extraction is introduced.Firstly,the ex-isting feature extraction methods are briefly summarized,including traditional feature extraction methods,deep learn-ing-based feature extraction methods and deep reinforcement learning-based feature extraction methods.Then,the deep reinforcement learning and image processing technology are described.Finally,the design method of automatic feature extraction model based on deep reinforcement learning is introduced in detail,including data set preparation and processing,model architecture design,model training process and parameter setting,analysis and evaluation of experimental results.This model can improve the accuracy and efficiency of image processing,and has a high popu-larization value.
关键词
深度强化学习/自动特征提取/图像处理/模型训练Key words
deep reinforcement learning/automatic feature extraction/image processing/model training引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据