Improved Learning Algorithm Based on Semi-Supervised Support Vector


Article Preview

Improved learning algorithm for branch and bound for semi-supervised support vector machines is proposed, according to the greater difference in the optimal solution in different semi-supervised support vector machines for the same data set caused by the local optimization. The lower bound of node in IBBS3VM algorithm is re-defined, which will be pseudo-dual function value as the lower bound of node to avoid the large amount of calculation of 0-1 quadratic programming, reducing the lower bound of each node calculate the time complexity; at the same time, in determining the branch nodes, only based on the credibility of the unlabeled samples without the need to repeatedly carry out the training of support vector machines to enhance the training speed of the algorithm. Simulation analysis shows that IBBS3VM presented in this paper has faster training speed than BBS3VM algorithms, higher precision and stronger robustness than the other semi-supervised support vector machines.



Key Engineering Materials (Volumes 474-476)

Edited by:

Garry Zhu




G. X. Peng and B. Li, "Improved Learning Algorithm Based on Semi-Supervised Support Vector", Key Engineering Materials, Vols. 474-476, pp. 1-6, 2011

Online since:

April 2011





[1] O. Chapelle, V. Sindhwani, S. Keerthi. Branch and Bound for Semi- supervised Support Vector Machine. Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, British, 2006: 217-224P.

[2] Wang J, Shen X, Pan W. On transductive support vector machines. In J. Verducci, X. Shen, and J. Lafferty, editors, Prediction and Discovery. American Mathematical Society, 2007: 7-15P.

[3] Wu M, Ye J. A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2009, 31(11): 2088-2092P.


[4] Lapray D, Bergeler J, Luhmann H J. Stimulus-induced gamma activity in the electrocorticogram of freely moving rats: The neuronal signature of novelty detection Behavioural Brain Research. 2009, 199(2): 350-354P.


[5] Mazzariello C, Sansone C. Anomaly-Based Detection of IRC Botnets by Means of One-Class Support Vector Classifiers. In proceedings of the 15th International Conference on Image Analysis and Processing, Vietri sul Mare, 2009: 892-905P.


[6] Schleif F M, Lindemann M, Diaz M. Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform. Computing and Visualization in Science. 2009, 12(4): 189-199P.


[7] Zanero S, Serazzi G. Unsupervised learning algorithms for intrusion detection. In IEEE Network Operations and Management Symposium, Osaka, 2008: 1043-1048P.


[8] Yeung D, Chow C. Parzen-window network intrusion detectors. In proceedings of the 16th international conference on pattern recognition, Québec, 2002: 385-388P.

[9] Campbell C, Bennett K P. A linear programming approach to novelty detection. Advances in neural information processing systems, Vancouver, 2001: 395-401P.

[10] Torres R S, Falc o A X, Gon alves M A, et al. A genetic programming framework for content-based image retrieval. Pattern Recognition. 2009, 42(2): 283-292P.

[11] Bishop C M. Neural networks for pattern recognition. Oxford University Press, (2005).

[12] Toosi A N, Kahani M. A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers. Computer Communications. 2007, 30(10): 2201-2212P.