J4 ›› 2010, Vol. 45 ›› Issue (11): 1-4.

• Articles •     Next Articles

Semi-supervised classification based on the Markov random field and robust error function

LIANG Jun1,2, CHEN Long2, ZHOU Wei-qi2, TAO Wen-qian1, YAO Ming2, XU Zheng-chuan3   

  1. 1. School of Computer Science &Telecommunication Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China;
    2. School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China;
    3. School of Management, Fudan University, Shanghai 200433, China
  • Received:2010-01-06 Online:2010-11-16 Published:2010-11-24


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 Markov random field. Also two algorithms based on the iterative condition mode and the Markov chain Monte 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.

Key words:  semi-supervised; classification; Markov random field; robust error function

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