Ensembles of Regularized Least Squares Classifiers for high-dimensional problems

Studies in Fuzziness and Soft Computing

Volumn 207, Issue , 2006, Pages 297-313

  Torkkola, Kari ^a   Tuv, Eugene ^b  

^a MOTOROLA INC   (United States)

^b INTEL CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34047105102     PISSN: 14349922     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-35488-8_12     Document Type: Article

References (20)

1. B. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In Proc. COLT, pages 144-152, 1992.
2. O. Bousquet and A. Elisseeff. Algorithmic stability and generalization performance. In Proc. NIPS, pages 196-202, 2000.
3. L. Breiman. Random forests. Machine Learning, 45(l):5-32, 2001.
4. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
5. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. CRC Press, 1984.
6. F. Cucker and S. Smale. On the mathematial foundations of learning. Bulletin of the American Mathematical Society, 89(l):l-49, 2001.
7. F. Cucker and S. Smale. Best choices for regularization parameters in learning theory: on the bias-variance problem. Foundations of Computational Mathematics, 2(4):413-428, 2003.
8. T. Evgeniou. Learning with Kernel Machine Architectures. PhD thesis, Massachusetts Institute of Technology, EECS, July 2000.
9. Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In European Conference on Computational Learning Theory, pages 23-37, 1995.
10. J.H. Friedman. Greedy function approximation: a gradient boosting machine. Technical report, Dept. of Statistics, Stanford University, 1999a.
11. J.H. Friedman. Stochastic gradient boosting. Technical report, Dept. of Statistics, Stanford University, 1999b.
12. A. Hoerl and R. Kennard. Ridge regression; biased estimation for nonorthogonal problems. Technometrics, 12(3):55-67, 1970.
13. V. Ng and L. Breiman. Random forest variable selection. In I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, editors, Feature Extraction, Foundations and Applications. Springer, New York, 2005. This volume.
14. T. Poggio and S. Smale. The mathematics of learning: Dealing with data. Notices of the American Mathematical Society (AMS), 50(5):537-544, 2003.
15. T. Poggio, R. Rifkin, S. Mukherjee, and A. Rakhlin. Bagging regularizes. CBCL Paper 214, Massachusetts Institute of Technology, Cambridge, MA, February 2002. AI Memo #2002-003.
16. R. Rifkin. Everything Old Is New Again: A Fresh Look at Historical Approaches in Machine Learning. PhD thesis, MIT, 2002.
17. B. Scholkopf and A. Smola. Learning with Kernels. MIT Press, 2002.
18. J.A.K. Suykens and J. Vandervalle. Least squares support vector machines. Neural Processing Letters, 9(3):293-300, June 1999.
19. A.N. Tikhonov and V.Y. Arsenin. Solutions of Ill-posed Problems. W.H.Wingston, Washington, D.C., 1977.
20. K. Torkkola. Feature extraction by non-parametric mutual information maximization. Journal of Machine Learning Research, 3:1415-1438, March 2003.