自动化学报2018，Vol.44Issue(2)：311-317,7.DOI:10.16383/j.aas.2018.c160818
基于极限学习机参数迁移的域适应算法
Domain Adaption Algorithm with ELM Parameter Transfer
许夙晖 1慕晓冬 1柴栋 2罗畅3
作者信息
- 1. 火箭军工程大学信息工程系 西安710025
- 2. 北京航空工程技术研究中心 北京100076
- 3. 空军工程大学防空反导学院 西安710051
- 折叠
摘要
Abstract
In allusion to transfer learning problem with a small number of labeled samples,a domain adaption method through transferring extreme learning machine (ELM) parameters is proposed in this paper.The core idea is projecting the target ELM parameters on to the source and making the parameters maximally aligned with the source.In addition,considering the transformation may cause negative transfer,a regular term is added to the objective function.Unlike the existing domain adaption method,the parameters of classifier and the transformation matrix can be calculated simultaneously,and the objective function can be easily solved.Experiments demonstrate the proposed method has potential advantages in terms of accuracy and efficiency compared to the state-of-the-art approaches.关键词
域适应/迁移学习/极限学习机/正则化/中层语义特征/深度特征Key words
Domain adaption/transfer learning/extreme learning machine/regularization/middle-level feature/deep feature引用本文复制引用
许夙晖,慕晓冬,柴栋,罗畅..基于极限学习机参数迁移的域适应算法[J].自动化学报,2018,44(2):311-317,7.
