基于同伴辅助学习分类器的部分域自适应方法
PARTIAL DOMAIN ADAPTIVE METHOD BASED ON PEER ASSISTED LEARNING CLASSIFIER
邱春红 1邵晓根2
作者信息
- 1. 江苏商贸职业学院 江苏南通 226011
- 2. 徐州工程学院 江苏徐州 221000
- 折叠
摘要
为了解决传统方法忽略分类器转移场景,进一步减轻负转移,提出一种基于同伴辅助学习分类器的部分域自适应方法.提出一个软加权最大均方差来减轻源异常域和目标域之间的负迁移,使得源共享域和目标域的特征分布在特征空间中是一致的;引入一种同伴辅助学习方法,减轻特定目标学习分类器的过度拟合问题.在三个数据集上的实验结果证明该方法不仅减轻了负迁移,而且解决了分类器移位问题.
Abstract
In order to solve the problem that traditional methods ignore the classifier transition scenario and further reduce the negative transfer,a partial domain adaptive method based on peer assisted learning classifier is proposed.A soft weighted maximum mean square deviation was proposed to reduce the negative transfer between the source domain and the target domain,so that the feature distribution of the source shared domain and the target domain was consistent in the feature space.A peer assisted learning method was introduced to reduce the over fitting problem of the specific target learning classifier.Experimental results on three data sets show that the proposed method not only reduces negative migration,but also solves the problem of classifier shift.
关键词
部分域自适应/负转移/分类器/同伴辅助学习Key words
Partial domain adaptive/Negative transfer/Classifier/Peer assisted learning引用本文复制引用
基金项目
江苏省高等学校自然科学基金面上项目(18KJB520015)
江苏省现代教育技术研究2018年度智慧校园专项课题(2018-R-66871)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据