摘要
In order to handle the semi-supervised problem quickly and efficiently in the twin support vector machine (TWSVM) field, a semi-supervised twin support vector machine (S2TSVM) is proposed by adding the original unlabeled samples. In S2TSVM, the addition of unlabeled samples can easily cause the classification hyper plane to deviate from the sample points. Then a centerdistance principle is proposed to pre-classify unlabeled samples, and a pre-classified S2TSVM (PS2TSVM) is proposed. Compared with S2TSVM, PS2TSVM not only improves the problem of the samples deviating from the classification hyper plane, but also improves the training speed. Then PS2TSVM is smoothed. After smoothing the model, the pre-classified smooth S2TSVM (PS3TSVM) is obtained, and its convergence is deduced. Finally, nine datasets are selected in the UCI machine learning database for comparison with other types of semi-supervised models. The experimental results show that the proposed PS3TSVM model has better classification results.
In order to handle the semi-supervised problem quickly and efficiently in the twin support vector machine(TWSVM) field,a semi-supervised twin support vector machine(S^2 TSVM) is proposed by adding the original unlabeled samples. In S^2 TSVM, the addition of unlabeled samples can easily cause the classification hyper plane to deviate from the sample points. Then a centerdistance principle is proposed to pre-classify unlabeled samples,and a pre-classified S^2 TSVM(PS^2 TSVM) is proposed. Compared with S^2 TSVM, PS^2 TSVM not only improves the problem of the samples deviating from the classification hyper plane, but also improves the training speed. Then PS^2 TSVM is smoothed.After smoothing the model, the pre-classified smooth S^2 TSVM(PS3 TSVM) is obtained, and its convergence is deduced. Finally,nine datasets are selected in the UCI machine learning database for comparison with other types of semi-supervised models. The experimental results show that the proposed PS3 TSVM model has better classification results.
基金
supported by the Fundamental Research Funds for University of Science and Technology Beijing(FRF-BR-12-021)
