Optimization of the SVM kernels using an empirical error minimization scheme

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2388, Issue , 2002, Pages 354-369

  Ayat, Nedjem Eddine ^a,b   Cheriet, Mohamed ^a   Suen, Ching Y ^b  

^a ÉCOLE DE TECHNOLOGIE SUPÉRIEURE   (Canada)

^b Civil and Environmental Engineering   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ERRORS; IMAGE RETRIEVAL; OPTIMIZATION; PATTERN RECOGNITION; VECTORS;

EMPIRICAL ERRORS; FASTER CONVERGENCE; GRADIENT DESCENT METHOD; KERNEL PARAMETER; OBJECTIVE FUNCTIONS; OPTIMIZATION SCHEME; POLYNOMIAL KERNELS; UPPER-BOUND OF THE ERROR;

SUPPORT VECTOR MACHINES;

EID: 84958774009     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45665-1_28     Document Type: Conference Paper

References (21)

1. N. E. Ayat, M. Cheriet, and C. Y. Suen. Un système neuro-flou pour la reconnaissance de montants numériques de chèques arabes. In CIFED, pages 171–180, Lyon, France, Jul. 2000.
2. N. E. Ayat, M. Cheriet, and C. Y. Suen. Kmod-a new support vector machine kernel for pattern recognition. application to digit image recognition. In ICDAR, pages 1215–1219, Seattle, USA, Sept. 2001.
3. N. E. Ayat, M. Cheriet, and C. Y. Suen. Kmod-a two parameter svm kernel for pattern recognition. to appear in icpr 2002. quebec city, canada, 2002, 2002.
4. Y. Bengio. Gradient-based optimization of hyper-parameters. Neural Computation, 12(8):1889–1900, 2000.
5. B. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In Fifth Annual Workshop on Computational Learning Theory, Pittsburg, 1992.
6. C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273–297, 1995.
7. Courant and R. Hilbert. Methods of Mathematical Physics. Interscience, 1953.
8. G. Wahba. The bias-variance trade-off and the randomized gacv. Advances in Neural Information Processing Systems, 11(5), November 1999.
9. T. Joachims. Making large-scale svm learning practical. In B. Scholkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods-Support Vector Learning, chapter 11. 1999.
10. [10] J. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3), October 1999.
11. [11] U. Kreβel. Pairwise classification and support vector machines. In B. Scholkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods-Support Vector Learning, chapter Chap. 15, pages 255–268. 1999.
12. [12] J. Larsen, C. Svarer, L. N. Andersen, and L. K. Hansen. Adaptive regularization in neural network modeling. In Neural Networks: Tricks of the Trade, pages 113–132, 1996.
13. [13] O. Chapelle and V. Vapnik. Choosing multiple parameters for support vector machines. Advances in Neural Information Processing Systems, 03(5), March 2001.
14. [14] W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C, the art of scientific computing. Cambridge University Press, second edition, 1992.
15. [15] G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for adaboost. Machine Learning, 43(3):287–320, 2001.
16. [16] B. Scholkopf. Support vector learning. PhD thesis, Universität Berlin, Berlin, Germany, 1997.
17. [17] B. Scholkopf, C. Burges, and A. Smola. Introduction to support vector learning. In B. Scholkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods-Support Vector Learning, chapter 1. 1999.
18. [18] P. Sollich. Bayesian methods for support vector machines: Evidence and predictive class probabilities. Machine Learning, 46(1/3):21, 2002.
19. [19] V. Vapnik. The Nature of Statistical Learning Theory. NY, USA, 1995.
20. [20] V. Vapnik. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), September 1999.
21. [21] D. S. Watkins. Fundamentals of Matrix Computations. Wiley, New York, 1991.