Semi-supervised classification based on the Markov random field and robust error function
XU Zheng-chuan · Journal of Shandong University · 2010
A model of semi-supervised classification was proposed to overcome the problem induced by mislabeled samples.A decision rule was learned from labeled and unlabeled data,and a new energy function based on robust error function was used in the M arkov random field.Also two algorithms based on the iterative condition mode and the M arkov chain M onte Carlo were designed to infer the label of both labeled and unlabeled samples.Experimental results demonstrated that the proposed methods were efficient for a real-world dataset.