PARTIAL DOMAIN ADAPTIVE METHOD BASED ON PEER ASSISTED LEARNING CLASSIFIER
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.
国家科技期刊平台
NSTL
万方数据
维普
