Support vector machine via nonlinear rescaling method

Optimization Letters

Volumn 1, Issue 4, 2007, Pages 367-378

  Polyak, Roman ^a   Ho, Shen Shyang ^b   Griva, Igor ^a  

^a GEORGE MASON UNIVERSITY   (United States)

^b Krasnow Institute for Advanced Study   (United States)

Author keywords

Convex optimization; Duality; Lagrange multipliers; Support vector machine

Indexed keywords

APPROXIMATION THEORY; CLASSIFICATION (OF INFORMATION); DATABASE SYSTEMS; LAGRANGE MULTIPLIERS; LEARNING ALGORITHMS; OPTIMIZATION; PROBLEM SOLVING;

CONVEX OPTIMIZATION; DUALITY; NONLINEAR RESCALING METHODS;

SUPPORT VECTOR MACHINES;

EID: 34948819142     PISSN: 18624472     EISSN: 18624480     Source Type: Journal    
DOI: 10.1007/s11590-006-0033-2     Document Type: Article

References (17)

1. Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. http://www.ics. uci.edu/~mlearn/MLRepository.html. Department of Information and Computer Sciences, University of California, Irvine (1998)
2. Boser, B.E., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: COLT, pp. 144-152. ACM, Pittsburg (1992)
3. Burges, C.J.C.: Simplified support vector decision rules. In: ICML, San Fransisco, pp. 71-77 (1996)
4. Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines (2001)
5. Cortes C., Vapnik V. (1995) Support-vector networks. Mach. Learn. 20, 273-297
6. Guyon, I., Matic, N., Vapnik, V.: Discovering informative patterns and data cleaning. In: Advances in knowledge discovery and data mining, pp. 181-203 (1996)
7. Jensen D., Polyak R. (1994) The convergence of a modified barrier method for convex programming. IBM J. Res. Dev. 38:307-321
8. Melman A., Polyak R. (1996) The Newton modified barrier method for QP problems. Ann. Oper. Res. 62, 465-519
9. Nesterov, Y., Nemirovskii, A.: Interior-point Polynomial Algorithms in Convex Programming. Society for Industrial and Applied Mathematics, Philadelphia (1994)
10. Nguyen, D., Ho, T.B.: An efficient method for simplifying support vector machine. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 617-624 (2005)
11. Polyak R. (1992) Modified barrier functions (theory and methods). Math. Program. 54, 177-222
12. Polyak R. (2002) Nonlinear rescaling vs smoothing technique in convex optimization. Math. Program. Ser. A 92, 197-235
13. Polyak R., Griva I. (2004) Primal-dual nonlinear rescaling method for convex optimization. J. Optim. Theory Appl. 122(1):111-156
14. Polyak R., Teboulle M. (1997) Nonlinear rescaling and proximal-like methods in convex optimization. Math. Program. 76, 265-284
15. Rätsch G., Onoda T., Müller K.-R. (2001) Soft margins for adaboost. Mach. Learn. 42, 287-320
16. Vapnik V.N. (2000) The nature of statistical learning theory, 2nd edn. Springer, Berlin Heidelberg New York
17. Wu M., Scholkopf B., Bakir G. (2006) A Direct Method for Building Sparse Kernel Learning Algorithms. J. Mach. Learn. Res. 7, 603-624