L.K.Luo*, X.D. Zhang, H.Peng, W.H. Lv, Y. Zhang. A
New Pruning Method for Decision Tree based on Structural Risk of Leaf Node.
Neural Computing and Application, Vol.22, Suppl 1., May 2013, pp.17-26. (SCI,
IF2012=1.168)
L.K.Luo*, X. Chen. Integrating Piecewise Linear
Representation and Weighted Support Vector Machine for Stock Trading Signal
Prediction. Applied Soft Computing, Vol. 13, No.2, February 2013, pp. 806-816.
(SCI, IF2010=2.097)
H.Peng, M.X.Luo, W.H.Lv, L.K.Luo*. A Soft-Margin
Support Vector Machine based on Normal Convex Hulls. International Journal of
Advancements in Computing Technology Volume 3, Number 7, August 2011,
pp.244-254.（EI）
L.K. Luo*, L.J. Ye, M.X. Luo, D.F. Huang, H. Peng,
and F. Yang. Methods of Forward Feature Selection based on the Aggregation of
Classifiers Generated by Single Attribute. Computers in Biology and
Medicine,Vol.41, No.7, pp.435-441,July,2011.（SCI, IF2009=1
L.K.Luo*, D.F.Huang, L.J.Ye, Q.F.Zhou, G.F.Shao,
and H.Peng. Improving the Computational Efficiency of Recursive Cluster
Elimination for Gene Selection. IEEE/ ACM Transactions on Computational Biology
and Bioinformatics, Vol.8, No.1, pp.122- 129,2011. (SC
L.K. Luo*, M.X. Luo, L.J.Ye, H. Peng, and F. Yang.
Maximum Robustness Criterion on Kernel Selection of Support Vector Machine.
Journal of Convergence Information Technology, Vol. 6, No.1, pp. 294-306, 2011.
(EI)
罗林开*,叶凌君,彭洪,杨帆.给定经验风险水平的支持向量回归机.华中科技大学学报
(自然科学版),2010,Vol.38(10):47-51. (EI)
L.K. Luo*, D.F. Huang, H. Peng, Q.F Zhou, G.F. Shao,
and F. Yang. A New Parameter Selection Method for Support Vector Machine Based
on the Decision Value. Journal of Convergence Information Technology, Vol. 5,
No. 8, pp. 36- 41, 2010. (EI)
罗林开,张晓东.L无穷范数软间隔支持向量分类机.东南大学学报(自然科学版
),Vol.40,Sup1,pp.234-237,2010年9月.(EI)
L.K.Luo*, T. Liu, H. Peng , and Q.F. Zhou.
Polynomial Radial Base Function: A New Local Similarity Measure. Proceedings of
the fourth International Conference on Natural Computation, Vol.2, pp. 116-119,
Oct. 2008. (EI)
L.K. Luo*, C.D. Lin, H. Peng and Q.F. Zhou. A Study
on Piecewise Polynomial Smooth Approximation to the Plus Function. Proceeding of
the 9th International Conference on Control, Automation, Robotics and Vision,
pp.2177-2182, Dec.2006. (EI)
张秋水,罗林开,刘晋闽. 基于支持向量机的中国上市公司财务困境预测. 计算机应 用,Vol26,
Supp.,pp.105-107, 2006年6月.
*Zhou Q, Zhou H, Zhou Q, Yang F, Luo L, 2014.
Structure damage detection based on random forest recursive feature elimination,
Mechanical Systems and Signal Processing, 46(2014): 82–90.
