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Ikeda K (2004) An asymptotic statistical theory of polynomial kernel methods Neural Comput 16:1705-1719

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Aizerman MA, Braverman EM, Rozonoer LI (1964) Theoretical foundations of the potential function method in pattern recognition learning Automation Remote Control 25:821-837
Amari S (1993) A universal theorem on learning curves Neural Netw 6:161-166
Amari S, Fujita N, Shinomoto S (1992) Four types of learning curves Neural Comput 4:605-618
Amari S, Murata N (1993) Statistical theory of learning curves under entropic loss criterion Neural Comput 5:140-154
Anlauf JK, Biehl M (1989) The AdaTron: An adaptive perceptron algorithm Europhys Lett 10:687-692
Baum E, Haussler D (1989) What size net gives valid generalization Neural Comput 1:151-160
Cristianini N, Shawe-taylor J (2000) An introduction to support vector machines
Dietrich R, Opper M, Sompolinsky H (1999) Statistical mechanics of support vector networks Phys Rev Lett 82:2975-2978
Friess T, Cristianini N, Campbell C (1998) The kernel adatron algorithm:A fast and simple learning procedure for support vector machine Proc. 15th International Conference on Machine Learning
Gyorgyi G, Tishby N (1990) Statistical theory of learning a rule Neural networks and spin glasses, Thuemann K:Koeberle R, ed. pp.31
Herbrich R (2002) Learning kernel classifiers: Theory and algorithms
Ikeda K (2003) Generalization error analysis for polynomial kernel methods algebraic geometrical approach Artificial neural networks and neural information processing, Kaynak O:Alpaydin E:Oja E:Xu L, ed. pp.201
Ikeda K (2004) Geometry and learning curves of kernel methods with polynomial kernels Systems And Computers In Japan 35:41-48
Ikeda K, Amari S (1996) Geometry of admissible parameter region in neural learning IEICE Trans. Fundamentals E79:938-943
Levin E, Tishby N, Solla SA (1990) A statistical approach to learning and generalization in layered neural networks Proc IEEE 78:1568-1674
Mitchell TM (1982) Generalization as search Artif Intell 18:203-226
Murata N, Yoshizawa S, Amari S (1994) Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Trans Neural Netw 5:865-72 [Journal] [PubMed]
Neal RM (1996) Bayesian learning for neural networks
Nishimori H (2001) Statistical physics of spin glasses and information processing: An introduction
Opper M, Haussler D (1991) Calculation of the learning curve of Bayes optimal classification on algorithm for learning a perceptron with noise Proc Ann Workshop Comp Learning Theory 4:75-87
Scholkopf B, Smola A, Muller KR (1999) Kernel principal component analysis Advances in kernel methods-Support vector learning, Scholkopf B:Burges C:Smola A, ed.
Scholkopf B, Smola AJ (2001) Learning with kernels: Support vector machines, regularization, optimization, and beyond
Smola AJ, Bartlett PL, Scholkopf B, Schuurmans D (2000) Advances in large margin classifiers, Smola AJ:Bartlett PL:Scholkopf B:Schuurmans D, ed.
Valiant LG (1984) A theory of the learnable Communications Of The ACM 27:1134-1142
Vapnik V (1995) The Nature of Statistical Learning Theory
Vapnik V (1998) Statistical Learning Theory
Vapnik VN, Chervonenkis AY (1971) On the uniform convergence of relative frequencies of events to their probabilities Theory Of Probability And Its Applications 16:264-280
Ikeda K, Murata N (2005) Geometrical properties of nu support vector machines with different norms. Neural Comput 17:2508-29 [Journal] [PubMed]
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