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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.11
- 수록면
- 1,707 - 1,719 (13page)
- DOI
- 10.7465/jkdi.2018.29.6.1707
이용수
초록· 키워드
오류제보하기
In this paper we propose robust support vector regression algorithms to deal with noisy data sets. We adopt the absolute deviation error function for a loss function of regression model, and the proposed algorithms preserves the structure of the least squares support vector regression. The proposed algorithms are very fast and the procedures are much simpler than other support vector machine algorithms. They are robust to regression outliers, because the loss functions are less increasing than the squares error function for large errors and it uses a weight function for each observation. By comparing the proposed algorithms with other methods for the simulated datasets and benchmark datasets, the proposed methods are more robust than the least squares support vector regression when outliers exist.
목차
Abstract
1. Introduction
2. Weighted least absolute deviation support vector regression
3. Computing Algorithm
4. Numerical Experiments
5. Conclusion
References
참고문헌 (35)
참고문헌 신청
[학술지(정기간행물)] - Amayri, O. / 2010 / A study of spam ltering using support vector machines / Articial Intelligence Review 34 : 73 ~ 108
[학술지(정기간행물)] - Argyriou, A. / 2009 / When is there a representer theorem? Vector versus matrix regularizers / Journal of Machine Learning Research 10 : 2507 ~ 2529
[학술지(정기간행물)] - Bao, Y. / 2013 / A PSO and pattern search based memetic algorithm for SVMs parameters optimization / Neurocomputing 117 : 98 ~ 106
[학술지(정기간행물)] - Cao, L. J. / 2003 / Support vector machine with adaptive parameters in nancial time series forecasting / IEEE Transactions on Neural Networks 14 : 1506 ~ 1518
[학술지(정기간행물)] - Chapelle, O. / 2007 / Training a support vector machine in the primal / Neural Computation 19 : 1155 ~ 1178
[학술지(정기간행물)] - Argyriou, A. / 2009 / When is there a representer theorem? Vector versus matrix regularizers / Journal of Machine Learning Research 10 : 2507 ~ 2529
[학술지(정기간행물)] - Bao, Y. / 2013 / A PSO and pattern search based memetic algorithm for SVMs parameters optimization / Neurocomputing 117 : 98 ~ 106
[학술지(정기간행물)] - Cao, L. J. / 2003 / Support vector machine with adaptive parameters in nancial time series forecasting / IEEE Transactions on Neural Networks 14 : 1506 ~ 1518
[학술지(정기간행물)] - Chapelle, O. / 2007 / Training a support vector machine in the primal / Neural Computation 19 : 1155 ~ 1178
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UCI(KEPA) : I410-ECN-0101-2019-041-000212273