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- 한국산업응용수학회 JOURNAL OF THE KOREAN SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS Journal of the Korean Society for Industrial and Applied Mathematics Vol.22 No.1
- 발행연도
- 2018.3
- 수록면
- 1 - 13 (13page)
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초록· 키워드
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Boosting, one of the most successful algorithms for supervised learning, searches the most accurate weighted sum of weak classifiers. The search corresponds to a convex programming with non-negativity and affine constraint. In this article, we propose a novel Conjugate Gradient algorithm with the Modified Polak-Ribiera-Polyak conjugate direction. The convergence of the algorithm is proved and we report its successful applications to boosting.
목차
ABSTRACT
1. INTRODUCTION
2. MATHEMATICAL FORMULATION OF BOOSTING
3. CONJUGATE GRADIENT METHOD
4. NUMERICAL RESULTS
5. CONCLUSION
REFERENCES
참고문헌 (11)
참고문헌 신청
Rich Caruana / 2006 / An empirical comparison of supervised learning algorithms / Proceedings of the 23rd international conference on Machine learning :161~168
Ayhan Demiriz / 2002 / Linear programming boosting via column generation / Machine Learning 46(1):225~254
Yoav Freund / 1995 / A desicion-theoretic generalization of on-line learning and an application to boosting / European conference on computational learning theory :23~37
Adam J Grove / 1998 / Boosting in the limit: Maximizing the margin of learned ensembles / AAAI/IAAI :692~699
Can Li / 2013 / A conjugate gradient type method for the nonnegative constraints optimization problems / Journal of Applied Mathematics 2013
Ayhan Demiriz / 2002 / Linear programming boosting via column generation / Machine Learning 46(1):225~254
Yoav Freund / 1995 / A desicion-theoretic generalization of on-line learning and an application to boosting / European conference on computational learning theory :23~37
Adam J Grove / 1998 / Boosting in the limit: Maximizing the margin of learned ensembles / AAAI/IAAI :692~699
Can Li / 2013 / A conjugate gradient type method for the nonnegative constraints optimization problems / Journal of Applied Mathematics 2013
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