1[1]Rosenblatt, F. and Frank. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Cornell Aeronautical Laboratory, Psychological Review, vol.65, pp.386-408, 1958.
2[2]Vapnik, V.. Statistical Learning Theory. New York:Wildy, 1998.
3[3]Vapnik, V. and A. Lerner. Pattern recognition using generalized portrait method. Automation and Remote Control, 1963,(24).
4[4]Vapnik, V. and A. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, vo1.16(2), pp.264-280, 1971.
5[5]Vapnik, V. The Nature of Statistical Learning Theory. Springer Verlag,New York., 1995
6[6]Boser, B.E., I.M. Guyon and V. Vapnik. A Training Algorithm for Optimal Margin Classifiers, Fifth Annual Workshop on Computational Learning Theory, ACM, 1992.
7[7]Funahashi, K.J. On the Approximate Realization of Continuous Mapping by Neural Networks, Neural Networks, vol.2, pp. 183-192,1989.
8[8]Anthony, M. and P. Bartlett. Learning in NeuralNetworks: Theoretical Foundations. Cambridge UniversityPress, 1999.
9[9]Smola, A.J. and B. Scho1kopf. A Tutorial on Support Vector Regression.Produced as part of the ESPRIT Working Group in Neural and Computation Learning II, NeuroCOLT2 Technical Report Series NC2-TR-1998-030,October 1998.
10[10]Schoelkopf, B., C.J.C. Burges, and A.J. Smola. Advances in kernel methods - support vector learning, MIT Press, Cambridge, MA. Poggio, T.(1975). On optimal nonlinear associative recall. Biological Cybernetics, vol. 19,pp.201-209, 1999.
