Feature selection using random probes and linear support vector machines

2005 ICSC Congress on Computational Intelligence Methods and Applications

Volumn 2005, Issue , 2005, Pages

  Chi, Hoi Ming ^b   Ersoy, Okan K ^a,b   Moskowitz, Herbert ^c  

^a IEEE

^b PURDUE UNIVERSITY   (United States)

^c PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; GAUSSIAN DISTRIBUTION; POLYNOMIALS; PROBLEM SOLVING; RANDOM PROCESSES; STATISTICAL METHODS; SUPPORT VECTOR MACHINES;

FEATURE SELECTION ALGORITHM; LINEAR SUPPORT VECTOR MACHINES; POLYNOMIAL REPRESENTATION;

FEATURE EXTRACTION;

EID: 33947650531     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. J. Brank, M. Grobelnik, N. Milic-Frayling, and D. Mladenic, "Feature selection using linear support vector machines," Technical Report, MSR-TR-2002-63, Microsoft Research, Microsoft Corporation, 2002.
2. H. Stoppiglia, G. Dreyfus, R. Dubois, and Y. Oussar, "Ranking a random feature for variable and feature selection," Journal of Machine Learning Research, vol. 3, 2003, pp. 1399-1414.
3. V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
4. S. Wang, W. Zhu, and Z. Liang, "Shape deformation: SVM regression and application to medical image segmentation," International Conference on Computer Vision (ICCV'01), Vancouver, B. C., Canada, vol. 2, 2001, pp. 209-216.
5. V. Vapnik, S. Golowich, and A. Smola, "Support vector method for function approximation, regression estimation, and signal processing," In: M. Mozer, M. Jordan, and T. Petsche (eds.): Neural Information Processing Systems, vol. 9., MIT Press, Cambridge, MA, 1997.
6. K. R. Muller, A. Smola, G. Ratsch, B. Scholkopf, J. Kohlmorgen, and V. Vapnik, "Predicting time series with support vector machines," International Conference on Artificial Neural Networks (ICANN), 1997, pp. 999-1004.
7. T. B. Trafalis and H. Ince, "Support vector machine for regression and applications to financial forecasting," International Joint Conference Neural Network (IJCNN), 2000, pp. 348-353.
8. A. Smola and B. Scholkopf, "A tutorial on support vector regression," NeuroCOLT2 Technical Report Series, NC2-TR-1998-030, 1998.
9. R. Fletcher, Practical Methods of Optimization. John Wiley and Sons, New York, 1987.
10. J. Zhu, S. Rosset, T. Hastie and R. Tibshirani, "1-norm support vector machines," Technical report, Stanford University, 2003.
11. R. Tibshirani, "Regression shrinkage and selection via the Lasso," Journal of Royal Statistical Society, Series B, 58:1, 1996, pp. 267-288.
12. th International Conference on Machine Learning, 1998, pp. 82-90.
13. 1 norm minimization in Gauss-Markov models," Journal of Surveying Engineering, vol. 129, no. 1, Feb. 2003, pp. 37-43.
14. J. Bi, K. P. Bennett, M. Embrechts, C. M. Breneman, and M. Song, "Dimensionality reduction via sparse support vector machines," Journal of Machine Learning Research, vol. 3, 2003, pp. 1229-1243.
15. H.-M. Chi, O. K. Ersoy, H. Moskowitz, and J. Ward, "Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms," European Journal of Operational Research (EJOR), submitted for publication.