Discrimination - Neural Networks: Methodology and Applications - page 377

7. Buhot A., Gordon M.B. [2000], Storage capacity of a constructive learning al-
gorithm, J. Phys. A 33, pp 1713-1727
8. Cover T.M. [1965], IEEE Trans. Elect. Comp. , 14, pp 326-334
9. Cover T.M., Thomas J. A. [1991], Elements of Information Theory , John Wiley
10. Cybenko G. [1989], Approximation by superpositions of a sigmoidal function,
Mathematics of Control, Signals and Sytems 2, pp 303-314
11. Dietrich R., Opper M., Sompolinsky H. [1999], Statistical Mechanics of Support
Vectors Networks, Phys. Rev. Lett. 82, pp 2975-2978
12. Engel A. and Van den Broeck C. [2001], Statistical Mechanics of Learning,
Cambridge University Press
13. Gardner E. [1989], J. of Physics A: Mathematical and General 22, N12, In the
honour of E. Gardner
14. Godin Ch. [2000], Contributions a l'embarquabiliteeta la robustesse des reseaux
de neurones en environnement radiatif, th`esedel'Ecole nationale superieure de
l'aeronautique et de l'espace, available from http://www-drfmc.cea.fr
15. Gordon M.B., Grempel D. [1995], Learning with a temperature dependant al-
gorithm. Europhys. Lett. 29, pp 257-262
16. Ho E., Kashyap R.L. [1965], An algorithm for linear inequalities and its appli-
cations, IEEE Transactions on Electronic Computers , 14, pp 683-688
17. Hopfield J.J. [1982], Proc. Natl. Acad. Sci. USA , 79, p. 2554
18. Krauth W., Mezard M. [1987], Learning algorithms with optimal stability in
neural networks, J. Phys. A 20, L745-L752
19. Risau-Gusman S., Gordon M.B. [2000a], Understanding stepwise generaliza-
tion of Support Vector Machines: a toy model, Advances in Neural Information
Processing Systems 12, S.A. Solla, T.K. Leen, K.-R. Muller (ed.), MIT Press,
pp 321-327
20. Risau-Gusman S., Gordon M.B. [2000b], Generalization properties of finite size
polynomial Support Vector Machines, Phys Rev E 62, pp 7092-7099
21. Risau-Gusman S., Gordon M.B. [2001], Statistical Mechanics of Soft Margin
Classifiers, Phys. Rev. E 64, 031907
22. Risau-Gusman S. [2001], Etude de proprietes d'apprentissage des machines
a exemples supports (SVM) par des methodes de physique statistique, these
de l'Universite de Grenoble I—Joseph-Fourier, available from http://www.uni-
bielefeld.de/ZIF/complexity/publications.html, ref. 2001/072
23. Risau-Gusman S., Gordon M.B. [2002], Hierarchical learning in polynomial sup-
port vector machines, Machine Learning 46, pp 53-70
24. Rosenblatt F. [1958], The perceptron: A probabilistic model for information
storage and organization in the brain, Phys. Rev. 65, p. 386
25. Torres Moreno J.M. [1997], Apprentissage et generalisation par des reseaux de
neurones: etude de nouveaux algorithmes constructifs, these de l'Institut na-
tional polytechnique de Grenoble, available from http://www-drfmc.cea.fr
26. Torres Moreno J.M., Gordon M. B. [1998], Characterization of the Sonar Signals
Benchmark, Neural Processing Letters 7, pp 1-4
27. Torres Moreno J.M. and Gordon M.B. [1998], E cient adaptive learning for
classification tasks with binary units , Neural Computation 10, pp. 1017-1040
28. Vapnik V. [1995], The Nature of Statistical Learning Theory , Springer