Learning properties of support vector machines with p-norm

Midwest Symposium on Circuits and Systems

Volumn 3, Issue , 2004, Pages

  Ikeda, Kazushi ^a   Murata, Noboru ^b  

^a KYOTO UNIVERSITY   (Japan)

^b WASEDA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; LINEAR PROGRAMMING; OPTIMIZATION; PROBLEM SOLVING; SET THEORY; VECTORS;

CLASSIFICATION TECHNIQUES; GENERALIZATION; QUADRATIC PROGRAMMING PROBLEMS; SUPPORT VECTOR MACHINES;

LEARNING SYSTEMS;

EID: 11144316920     PISSN: 15483746     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, 1995.
2. V. N. Vapnik, Statistical Learning Theory, John Wiley and Sons, 1998.
3. B. Schölkopf, C. Burges, and A. J. Smola, Advances in Kernel Methods: Support Vector Learning, Cambridge Univ. Press, 1998.
4. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge Univ. Press, 2000.
5. A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, Eds., Advances in Large Margin Classifiers, MIT Press, 2000.
6. O. L. Mangasarian, "Arbitrary-norm separating plane", Operations Research Letters, vol. 24, pp. 15-23, 1999.
7. Joãn P. Pedroso and Noboru Murata, "Support vector machines with different norms: Motivation, formulations, and results", Pattern Recognition Letters, vol. 22, no. 12, pp. 1263-1272, 2001.
8. Bernhard Schölkopf, Alexander Smola, R. Williamson, and P. L. Bartlett, "New support vector algorithms". Neural Computation, vol. 12, no. 5, pp. 1207-1245, 2000.
9. K. P. Bennett and E. J. Bredensteiner, "Duality and geometry in SVM classifiers", Prof. ICML, pp. 57-64, 2000.
10. Kazushi Ikeda and Tsutomu Aoishi, "An asymptotic statistical analysis of support vector machines with soft margins". Tech. Rep. NC2003-221, IEICE, 2004.
11. K. Ikeda and S. Amari, "Geometry of admissible parameter region in neural learning", IEICE Trans. Fundamentals, vol. E79-A, pp. 938-943, 1996.
12. Ralf Herbrich, Learning Kernel Classifiers: Theory and Algorithms, MIT Press, 2002.