Eliminating vertical stripe defects on silicon steel surface by L 1/2 regularization

Mathematical Problems in Engineering

Volumn 2011, Issue , 2011, Pages

  Jing, Wenfeng ^a   Meng, Deyu ^a   Qiao, Chen ^a   Peng, Zhiming ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION METHODS; COST-SENSITIVE; DATA SETS; ELECTROMAGNETIC PROPERTIES; GRADIENT BOOSTING; INCONSISTENT DATA; LINEAR CLASSIFIERS; LOSS FUNCTIONS; PRODUCTION ENVIRONMENTS; REGULARIZATION APPROACH; STEEL PRODUCTION; STEEL PRODUCTS; SUPPORT DEGREE; TECHNICAL PROBLEM; VERTICAL STRIPE;

CLASSIFICATION (OF INFORMATION); SILICON STEEL; STEELMAKING;

SURFACE DEFECTS;

EID: 84855570036     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2011/854674     Document Type: Article

References (15)

1. Xia Z. S., Reseach and production of boron bearing cold rolled non-oriented silicon steel Shanghai Metals 2003 25 4 20 23
2. Lin Y., Lee Y., Wahba G., Support vector machines for classification in nonstandard situations Machine Learning 2002 46 13 191 202 (Pubitemid 34129968)
3. Hosmer D. W., Lemeshow S., Applied Logistic Regression 2000 Hoboken, NJ, USA John Wiley Sons
4. Liu X. Y., Zhou Z. H., Training cost-sensitive neural networks with methods addressing the class imbalance problem IEEE Transactions on Knowledge and Data Engineering 2006 18 1 63 77
5. Fung G., Dundar M., Bi J., A fast iterative algorithm for Fisher discriminant using heterogeneous kernels Proceedings of the 21st international conference on Machine learning (ICML '04) 2004 Banff, AB, Canada
6. Zhang W. X., Mi J. S., Wu W. Z., Approaches to knowledge reductions in inconsistent systems International Journal of Intelligent Systems 2003 18 9 989 1000
7. He H. B., Edwardo A., Learning from imbalanced data IEEE Transactions on Knowledge and Data Engineering In Knowledge and Data Engineering 2009 21 9 1263 1284
8. McCarthy K., Zabar B., Weiss G. M., Does cost-sensitive learning beat sampling for classifying rare classes? Proceedings of the 1st International Workshop on Utility-Based Data Mining 2005 69 77
9. Liu X. Y., Zhou Z. H., The influence of class imbalance on cost-sensitive learning: an empirical study Proceedings of the 6th International Conference on Data Mining (ICDM '06) 2006 Hong Kong 970 974
10. Xu Z. B., Zhang H., Wang Y., Chang X., Liang Y., L12 regularization Science in China F: Information Sciences 2010 53 6 1159 1169
11. Tibshirani R., Regression shrinkage and selection via the Lasso Journal of the Royal Statistical Society. Series B. Methodological 1996 58 1 267 288
12. Vapnik V., The Nature of Statistical Learning Theory 1995 New York, NY, USA Springer-Verlag
13. Sun Y., Cost-sensitive boosting for classification of imbalanced data, Ph.D. thesis 2007 Waterloo, ON, Canada University of Waterloo
14. Rosset S., Zhu J., Hastie T., Boosting as a regularized path to a maximum margin classifier Journal of Machine Learning Research 2003/04 5 941 973
15. Hastie T., Tibshirani R., Friedman J., The elements of statistical learning: data mining Inference and Prediction 2001 New York, NY, USA Springer-Verlag