Sparse training procedure for kernel neuron

Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03

Volumn 1, Issue , 2003, Pages 49-53

  Xu, Jianhua ^a   Zhang, Xuegong ^b   Li, Yanda ^b  

^a NANJING NORMAL UNIVERSITY   (China)

^b TSINGHUA UNIVERSITY   (China)

Author keywords

Kernel neuron; Regularization; Sparseness; Support vector machine

Indexed keywords

EMPIRICAL RISKS; GENERALIZATION ABILITY; GRADIENT DESCENT METHOD; KERNEL FISHER DISCRIMINANT ANALYSIS; KERNEL MACHINE; KERNEL NEURON; MCCULLOCH-PITTS; MERCER KERNEL; NONLINEAR CLASSIFICATION; REGRESSION PROBLEM; REGULARIZATION; SPARSENESS; TRAINING ALGORITHMS; TRAINING PROCEDURES;

DISCRIMINANT ANALYSIS; FACE RECOGNITION; FISHER INFORMATION MATRIX; GEARS; LEARNING ALGORITHMS; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES;

NEURAL NETWORKS;

EID: 33644953785     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNNSP.2003.1279210     Document Type: Conference Paper

References (19)

1. W. S. McCulloch, W. Pitts, "A logical calculus of the ideas immanent in nervous activity", Bulletin of Mathematical Biophysics 5, 115-131, 1943.
2. F. Rosenblatt, "The perception: probabilistic model for information storage and organization in the brain", Psychological Review, 65(6), 386-408, 1958.
3. D. E. Rumelhart, G E. Hinton, R. J. Williams, "Learning representations by back-propagating errors", Nature, 323(9), 533-536, 1986.
4. D.F. Spechts "Generation of polynomial discriminant function for pattern recognition", IEEE Transactions on Electronic Computer, EC-16, 308-319, 1967.
5. S. Theodoridis, K, Koutroumbas, Pattern Recognition, Academic Press, San Diego, 1999.
6. C. Cortes, V. M. Vapnik, "Support vector networks", Machine Learning, 20(3), 273-297, 1995.
7. V. N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
8. V. N. Vapnik, The Nature of Statistical Learning Theory (2nd ed.), Springer-Verlag, New York, 1999.
9. S. Mika, G Ratsch, J. Weston, B. Scholkopf, K-R. Muller, "Fisher discriminant analysis with kernels", Proceedings of Neural Networks for Signal Processing IX, 41-48, IEEE Press, New York, 1999.
10. J. Xu, X. Zhang, Y. Li, "Large margin kernel pocket algorithm", Proceedings of IJCNN2001, 1480-1485, Washington DC, IEEE Press, 2001.
11. th International Conference on Neural Information Processing, Vol. 2, 861-866, Shanghai China, Fudan University Press, 2001.
12. A. N. Tikhonov, V. Y. Arsenin, Solution of Ill-posed Problem, W. H. Wanston, Washington DC, 1977.
13. K. Saito, R. Nakano, "Second order learning algorithm with squared penalty term", Neural Computation, 12(3), 709-729, 2000.
14. R. Reed, "Pruning algorithms - a survey", IEEE Transactions On Neural Networks, 4(5), 740-747, 1993.
15. P. M. Williams, "Bayesian Regularization and pruning using a Laplace prior", Neural Computation, 7(1), 117-143, 1995.
16. M. Ishikawa, "Structural learning with forgetting", Neural Networks, 9(3), 509-521, 1992.
17. D.C. Pault, S. J. Nowlan, G E. Hinton, "Experiments on learning by back propagation", Technical Report, CMU-CS-86-126, Camegie-Mallon University, 1986.
18. J. A. K. Suykens, L. Lukas, J. Vandewalle, "Sparse least squares support vector machines classifiers", In 8th European Symposium on Artificial Neural Networks (ESANN'2000), 37-42, 2000.
19. T. Joachims, "Making large-scale SVM learning practical", Advances in Kernel Methods - Support Vector Learning, Scholkopf B, Burges C, and Smola A (ed), 169-184, Cambridge MA, MIT Press, 1999.