Ikeda K. (2004). An asymptotic statistical theory of polynomial kernel methods Neural Comput. 16

See more from authors: Ikeda K

References and models cited by this paper

Aizerman MA, Braverman EM, Rozonoer LI. (1964). Theoretical foundations of the potential function method in pattern recognition learning Automation Remote Control. 25

Amari S. (1993). A universal theorem on learning curves Neural Netw. 6

Amari S, Murata N. (1993). Statistical theory of learning curves under entropic loss criterion Neural Comput. 5

Baum E, Haussler D. (1989). What size net gives valid generalization Neural Comput. 1

Biehl M, Anlauf JK. (1989). The AdaTron: An adaptive perceptron algorithm Europhys Lett. 10

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.

Haussler D, Opper M. (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

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.

Ikeda K. (2004). Geometry and learning curves of kernel methods with polynomial kernels Systems And Computers In Japan. 35

Ikeda K, Amari S. (1996). Geometry of admissible parameter region in neural learning IEICE Trans. Fundamentals. E79

Mitchell TM. (1982). Generalization as search Artif Intell. 18

Murata N, Yoshizawa S, Amari S. (1994). Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE transactions on neural networks. 5 [PubMed]

Neal RM. (1996). Bayesian learning for neural networks.

Nishimori H. (2001). Statistical physics of spin glasses and information processing: An introduction.

Scholkopf B, Smola A, Muller KR. (1999). Kernel principal component analysis Advances in kernel methods-Support vector learning.

Scholkopf B, Smola AJ. (2001). Learning with kernels: Support vector machines, regularization, optimization, and beyond.

Scholkopf B, Smola AJ, Bartlett PL, Schuurmans D. (2000). Advances in large margin classifiers.

Shawe-taylor J, Cristianini N. (2000). An introduction to support vector machines.

Shinomoto S, Amari S, Fujita N. (1992). Four types of learning curves Neural Comput. 4

Sompolinsky H, Opper M, Dietrich R. (1999). Statistical mechanics of support vector networks Phys Rev Lett. 82

Tishby N, Gyorgyi G. (1990). Statistical theory of learning a rule Neural networks and spin glasses.

Tishby N, Solla SA, Levin E. (1990). A statistical approach to learning and generalization in layered neural networks Proc IEEE. 78

Valiant LG. (1984). A theory of the learnable Communications Of The ACM. 27

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

References and models that cite this paper

Ikeda K, Murata N. (2005). Geometrical properties of nu support vector machines with different norms. Neural computation. 17 [PubMed]

This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.