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Transductive Support Vector Machine(TSVM) is one of semi-supervised learning algorithms which exploit the domain structure of the whole data by considering labeled and unlabeled data together. Although it was proposed several years ago, there has been no efficient algorithm which can handle problems with more than hundreds of training examples. In this paper, we propose an efficient branch-and-bound algorithm which can solve large-scale TSVM problems with thousands of training examples.
The proposed algorithm uses two bounding techniques: min-cut bound and reduced SVM bound. The min-cut bound is derived from a capacitated graph whose cuts represent a lower bound to the optimal objective function value of the dual problem. The reduced SVM bound is obtained by constructing the SVM problem with only labeled data. Experimental results show that the accuracy rate of TSVM can be significantly improved by learning from the optimal solution of TSVM, rather than an approximated solution.
The proposed algorithm uses two bounding techniques: min-cut bound and reduced SVM bound. The min-cut bound is derived from a capacitated graph whose cuts represent a lower bound to the optimal objective function value of the dual problem. The reduced SVM bound is obtained by constructing the SVM problem with only labeled data. Experimental results show that the accuracy rate of TSVM can be significantly improved by learning from the optimal solution of TSVM, rather than an approximated solution.
목차
Abstract
1. 서론
2. SVM과 TSVM
3. 한계전략
4. 분지전략
5. 분지한계 알고리즘
6. 실험결과
7. 결론 및 추후 과제
참고문헌
참고문헌 (17)
참고문헌 신청
[학술지(정기간행물)] - / 1998 / Semi-supervised support vector machines 10
[학술지(정기간행물)] - / 2004 / Convex Methods for Transduction 16
[학술지(정기간행물)] - / 2001 / Learning from Labeled and Unlabeled Data using Graph Mincuts : 2001 ~
[학술지(정기간행물)] - / 2001. / LIBSVM：A Library for Support Vector Machines
[학술지(정기간행물)] - / 2003 / Cluster Kernels for Semi-supervised Learning 15
[학술지(정기간행물)] - / 2004 / Convex Methods for Transduction 16
[학술지(정기간행물)] - / 2001 / Learning from Labeled and Unlabeled Data using Graph Mincuts : 2001 ~
[학술지(정기간행물)] - / 2001. / LIBSVM：A Library for Support Vector Machines
[학술지(정기간행물)] - / 2003 / Cluster Kernels for Semi-supervised Learning 15
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UCI(KEPA) : I410-ECN-0101-2009-325-015540482