title: Semi-supervised learning based on high density region estimation.
abstract: In this paper, we consider local regression problems on high density regions. We propose a semi-supervised local empirical risk minimization algorithm and bound its generalization error. The theoretical analysis shows that our method can utilize unlabeled data effectively and achieve fast learning rate.
authors.affiliation: Statistics Research Institute, College of Science, Huazhong Agricultural University, Wuhan 430070, China. chenhongml@163.com
authors.forename: Hong
authors.initials: H
authors.lastname: Chen
authors.forename: Luoqing
authors.initials: L
authors.lastname: Li
authors.forename: Jiangtao
authors.initials: J
authors.lastname: Peng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007858:Learning; D016571:Neural Networks, Computer; D012044:Regression Analysis