Semi-supervised learning based on high density region estimation.
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.
Chen
Hong
H
Statistics Research Institute, College of Science, Huazhong Agricultural University, Wuhan 430070, China. chenhongml@163.com
Li
Luoqing
L
Peng
Jiangtao
J
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007858:Learning; D016571:Neural Networks, Computer; D012044:Regression Analysis