Journal of Computers, Vol 6, No 7 (2011), 1438-1443, Jul 2011
doi:10.4304/jcp.6.7.1438-1443

A Semi-Supervised Learning Algorithm Based on Modified Self-training SVM

Yun Jin, Chengwei Huang, Li Zhao

Abstract


In this paper, we first introduce some facts about semi-supervised learning and its often used methods such as generative mixture models, self-training, co-training and Transductive SVM and so on. Then we present a self-training semi-supervised SVM algorithm based on which we give out a modified algorithm. In order to demonstrate its validity and effectiveness, we carry out some experiments which prove that our method is better than the former algorithm. Using our modified self-training semi-supervised SVM algorithm, we can save much time for labeling the unlabelled data and obtain a better classifier with good performance.


Keywords


semi-supervised learning; self-training;SVM; UCI

References


[1]X. Zhu, "Semi-Supervised Learning Literature Survey," Department of Computer Sciences, University of Wisconsin at Madison, Madison, WI2008.

[2]O'Neill and T.J, "Normal discrimination with unclassified observations," Journal of American Statistical Association, vol. 73, pp. 821-826, 1978.
http://dx.doi.org/10.2307/2286287

[3]K. Nigam, et al., "Text classificaiton from labeled and unlabeled documents using EM," Machine Learning, vol. 39(2/3), pp. 103-134, 2000.
http://dx.doi.org/10.1023/A:1007692713085

[4]C. Rosenberg, et al., "Semi-supervised self-training of object detection models," Seventh IEEE Workshop on Applications of Computer Vision, 2005.

[5]A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training.," Proc. Conf. on Computational Learning Theory, pp. 92-100, 1998.

[6]O. Chapelle and A. Zien, "Semi-supervised classfication by low density separtion.," Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.

[7]D. Zhou, et al., "Semi-supervised learning on directed graphs," Advances in Neural Information Processing Systems, vol. 16, 2004.

[8]F. G. Cozman, et al., "Semi-Supervised Learning of Mixture Models," Proceedings of the Twentieth International Conference on Machine Learning(ICML-2003), 2003.

[9]V. Vapnik, "Statistical Learning Theory," Springer, 1998.

[10]L. Didaci and F. Roli, "Using Co-training and Self-training in Semi-supervised Multiple Classifier System," Lecture Notes in Computer Science, vol. 4109, pp. 522-530, 2006.
http://dx.doi.org/10.1007/11815921_57

[11]C. G. Yuanqing Li, Huiqi Li, Zhengyang Chin, "A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system," Pattern Recognition Letters, vol. 29, pp. 1285-1294, 2008.
http://dx.doi.org/10.1016/j.patrec.2008.01.030

[12]I. P. Irene Kotsia, "Facial Expression Recognition in Image Sequences Using Geometric Dformation Features and Support Vector Machines," IEEE Transactions on Image Processing, vol. 16, 2007.

[13]C.-J. L. Chih-Chung Chang, "LIBSVM: a Library for Support Vector Machines," Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/, 2006.


Full Text: PDF


Journal of Computers (JCP, ISSN 1796-203X)

Copyright @ 2006-2012 by ACADEMY PUBLISHER – All rights reserved.