It has great practical significance to design a classifier on a small dataset(target domain) with the help of a large dataset(source domain). Since feature distribution varies on different datasets, the classifiers trained on the source domain cannot perform well on a target domain. To solve the problem, we propose a novel classifier-designing algorithm based on transfer learning theory. Firstly, to improve the compass of the same category and separateness of different categories in the source domain, this ...
引用格式舒醒,于慧敏,郑伟伟,谢奕,胡浩基,唐慧明.基于边际Fisher准则和迁移学习的小样本集分类器设计算法.自动化学报,2016,42(9):1313-1321Classifier-designing Algorithm on a Small Dataset Based onMargin Fisher Criterion and Transfer LearningSHU Xing1YU Hui-Min1,2
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