Kernel sample space projection classifier for pattern recognition

Proceedings - International Conference on Pattern Recognition

Volumn 2, Issue , 2004, Pages 435-438

  Washizawa, Yoshikazu ^a   Yamashita, Yukihiko ^b  

^a TOSHIBA CORPORATION   (Japan)

^b TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

HANDWRITTEN DIGITS RECOGNITION; KERNEL SAMPLE SPACE PROJECTION CLASSIFIER (KSP); MERCER KERNEL FUNCTION; SUPPORT VECTOR MACHINE (SVM);

COMPUTATIONAL COMPLEXITY; DATABASE SYSTEMS; FUNCTIONS; MATRIX ALGEBRA; POLYNOMIAL APPROXIMATION; PRINCIPAL COMPONENT ANALYSIS; VECTORS;

PATTERN RECOGNITION;

EID: 10044244745     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2004.1334247     Document Type: Conference Paper

References (10)

1. G. Baudat and F. Anouar. Feature vector selection and projection using kernels. Neurvcomputing, 55(1-2):21-38, 2003.
2. C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273-297, 1995.
3. S. Haykin. Neural Networks: A Comprehensive foundation. IEEE Computer Society Press, 1995.
4. T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 169-185. MIT Press, 1998.
5. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.-R. Müller. Fisher discriminant analysis with kernels. In Y.-H. Hu, J. Larsen, E. Wilson, and S. Douglas, editors. Neural Networks for Signal Processing IX, pages 41-48. IEEE, 1999.
6. K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2):181-201, 2001.
7. G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for Ada-Boost. Machine Learning, 42(3):287-320, Mar. 2001. also NeuroCOLT Technical Report NC-TR-1998-021.
8. G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for Ada-Boost. Machine Learning, 42(3):287-320, Mar. 2001. also NeuroCOLT Technical Report NC-TR-1998-021.
9. B. Schölkopf, A. Smola, and K.-R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5):1299-1319, 1998.
10. K. Tsuda. Subspace classifier in Hilbert space. Pattern Recognition Letters, 5(20):513-519, May 1999.