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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국경영과학회 Management Science and Financial Engineering International Journal of Management Science Vol.16 No.1
- 발행연도
- 2010.5
- 수록면
- 81 - 117 (37page)
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초록· 키워드
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Semi-supervised learning incorporates unlabeled examples, whose labels are unknown, as well as labeled examples into learning process. Although transductive support vector machine (TSVM), one of semi-supervised learning models, was proposed about a decade ago, its application to large-scaled data has still been limited due to its high computational complexity. Our previous research addressed this limitation by introducing a branch-and-bound algorithm for finding an optimal solution to TSVM.
In this paper, we propose three new techniques to enhance the performance of the branch-and-bound algorithm. The first one tightens min-cut bound, one of two bounding strategies. Another technique exploits a graph-based approximation to a support vector machine problem to avoid the most time-consuming step. The last one tries to fix the labels of unlabeled examples whose labels can be obviously predicted based on labeled examples. Experimental results are presented which demonstrate that the proposed techniques can reduce drastically the number of subproblems and eventually computational time.
In this paper, we propose three new techniques to enhance the performance of the branch-and-bound algorithm. The first one tightens min-cut bound, one of two bounding strategies. Another technique exploits a graph-based approximation to a support vector machine problem to avoid the most time-consuming step. The last one tries to fix the labels of unlabeled examples whose labels can be obviously predicted based on labeled examples. Experimental results are presented which demonstrate that the proposed techniques can reduce drastically the number of subproblems and eventually computational time.
목차
ABSTRACT
1. Introduction
2. Support Vector Machines(SVM) and Transductive Support Vector Machines(TSVM)
3. A branch-and-bound algorithm for TSVM(BBTSVM)
4. Tightening Min-cut Bounds
5. Fixing the Label of an Unlabeled Example Early
6. Implementation and experimental results
7. Conclusion
References
참고문헌 (24)
참고문헌 신청
1. [인터넷자원] - Asuncion, A / / UCI Machine Learning Repository
2. [단행본] - Bennett, K. P / 1998 / Semi-supervised support vector machines, in: Advance in Neural Information Processing Systems(NIPS) 10 / MIT Press
3. [단행본] - Bie, T / 2004 / Convex methods for transduction. in: Advances in Neural Information Processing Systems(NIPS) 16 / MIT Press
4. [학술대회논문] - Blum, A / 1998 / Combining labeled and unlabeled data with co-tra-ining / COLT: Proceedings of the Workshop on Computational Learning Theory
5. [학술대회논문] - Blum, A / 2001 / Learning from labeled and unlabeled data using graph mincuts / Proceedings of the 18th International Conference on Machine Lear-ning(ICML)
2. [단행본] - Bennett, K. P / 1998 / Semi-supervised support vector machines, in: Advance in Neural Information Processing Systems(NIPS) 10 / MIT Press
3. [단행본] - Bie, T / 2004 / Convex methods for transduction. in: Advances in Neural Information Processing Systems(NIPS) 16 / MIT Press
4. [학술대회논문] - Blum, A / 1998 / Combining labeled and unlabeled data with co-tra-ining / COLT: Proceedings of the Workshop on Computational Learning Theory
5. [학술대회논문] - Blum, A / 2001 / Learning from labeled and unlabeled data using graph mincuts / Proceedings of the 18th International Conference on Machine Lear-ning(ICML)
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