Research on Small-sample Model Generalization Performance Optimization Based on Meta-Learning and Data Enhancement
To address the issue of insufficient generalization performance in small sample models,a Meta-Learning mechanism is introduced to construct a data analysis model with strong generalization.A BP neural network is used to establish a data analysis model,and Model-Agnostic Meta-Learning Algorithm(MAML)is used to optimize the model.The results show that compared to traditional models such as Support Vector Machine and Gaussian process methods,the model established in this paper has better generalization performance.Regarding the training data format of MAML,a data augmentation method is introduced to increase the amount of training data.The root mean square error,average absolute percentage error,and deciding coefficient of the model established in the paper are 0.05,0.066,and 0.85,respectively,which are superior to other prediction models.
国家科技期刊平台
NSTL
万方数据
维普
