Towards making co-training suffer less from insufficient views

作     者：Xiangyu GUO Wei WANG 

作者机构：National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjing 210023China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2019年第13卷第1期

页      面：99-105页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：国家自然科学基金 the Fundamental Research Funds for the Central Universities the Collaborative Innovation Center of Novel Software Technology and Industrialization 

主　　题：semi-supervised learning co-training insufficient views 

摘      要：Co-training is a famous semi-supervised learning algorithm which can exploit unlabeled data to improve learning *** it works under a two-view setting (the input examples have two disjoint feature sets in nature),with the assumption that each view is sufficient to predict the ***,in real-world applications due to feature corruption or feature noise,both views may be insufficient and co-training will suffer from these insufficient *** this paper,we propose a novel algorithm named Weighted Co-training to deal with this *** identifies the newly labeled examples that are probably harmful for the other view,and decreases their weights in the training set to avoid the *** experimental results show that Weighted Co-training performs better than the state-of-art co-training algorithms on several benchmarks.