|
|
|
| Co-Training Semi-Supervised Active Learning Algorithm with Noise Filter |
| ZHAN Yong-Zhao, CHEN Ya-Bi |
| School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang 212013 |
|
|
|
|
Abstract The classification performance of the classifier based on semi-supervised learning is weakened when the noise samples are introduced. An algorithm called co-training semi-supervised active learning with noise filter is presented to overcome this disadvantage. In this algorithm, three fuzzy buried Markov models are used to perform semi-supervised learning cooperatively. Some human-computer interactions are actively introduced into labelling the unlabeled sample at certain time in order to avoid the rejective judgment when the classifiers do not agree with each other and the inaccurate judgment when the initial weak classifiers all agree. Meanwhile, the noise filter is used to filter the possible noise samples which are labeled automatically by the computer. The proposed algorithm is applied to facial expression recognition. The experimental results show that the algorithm can effectively improve the utilization of unlabeled samples, reduce the introduction of noise samples and raise the accuracy of expression recognition.
|
|
Received: 12 January 2009
|
|
|
|
|
|
[1] Zhu Xiaojin. Semi-Supervised Learning Literature Survey. Technical Report, 1530, Madison, USA: University of Wisconsin at Madison. Department of Computer Sciences, 2006
[2] Blum A, Mitchell T. Combining Labeled and Unlabeled Data with Co-Training // Proc of the 11th Annual Conference on Computational Learning Theory. Madison, USA, 1998: 92-100
[3] Goldman S A, Zhou Yan. Enhancing Supervised Learning with Unlabeled Data // Proc of the 17th International Conference on Machine Learning. Stanford, USA, 2000: 327-334
[4] Zhou Zhihua, Li Ming. Tri-Training: Exploiting Unlabeled Data Using Three Classifiers. IEEE Trans on Knowledge and Data Engineering, 2005, 17(11): 1529-1541
[5] Lewis D D, Gale W A. A Sequential Algorithm for Training Text Classifier // Proc of the 17th International Conference on Research and Development in Information Retrieval. Dublin, Ireland, 1994: 3-12
[6] Kothari R, Jain V. Learning from Labeled and Unlabeled Data Using a Minimal Number of Queries. IEEE Trans on Neural Networks, 2003, 14(6): 1496-1505
[7] Chen Yaodong, Wang Ting, Chen Huowang. Combining Semi-Supervised Learning and Active Learning for Shallow Semantic Parsing. Journal of Chinese Information Processing, 2008, 22(2): 70-75 (in Chinese)
(陈耀东,王 挺,陈火旺.半监督学习和主动学习相结合的浅层语义分析.中文信息学报, 2008, 22(2): 70-75)
[8] Zhan Yongzhao, Ye Jingfu, Niu Dejiao, et al. Facial Expression Recognition Based on Gabor Wavelet Transformation and Elastic Templates Matching. International Journal of Image and Graphics, 2006, 6(1): 125-138
[9]Cheng Keyang, Wen Chuanjun, Zhan Yongzhao.Research on Fuzzy Buried Markov Model. Computer Science, 2008, 35(6):163-167 (in Chinese)
(成科扬,文传军,詹永照.模糊深隐马尔可夫模型研究.计算机科学, 2008, 35(6): 163-167)
[10] Bilmes J A. Buried Markov Models for Speech Recognition // Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, USA, 1999: 713-716 |
|
|
|
2009, Vol. 22 
