Geometrical Properties of Nu Support Vector Machines with Different Norms
暂无分享,去创建一个
[1] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[2] Kazushi Ikeda,et al. Geometry of Admissible Parameter Region in Neural Learning , 1996 .
[3] Kazushi Ikeda,et al. Geometry and learning curves of kernel methods with polynomial kernels , 2004, Systems and Computers in Japan.
[4] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[5] Ralf Herbrich,et al. Learning Kernel Classifiers: Theory and Algorithms , 2001 .
[7] Kazushi Ikeda,et al. Effects of Soft Margins on Learning Curves of Support Vector Machines , 2004 .
[8] Jinbo Bi,et al. A geometric approach to support vector regression , 2003, Neurocomputing.
[9] Nello Cristianini,et al. An introduction to Support Vector Machines , 2000 .
[10] Olvi L. Mangasarian,et al. Arbitrary-norm separating plane , 1999, Oper. Res. Lett..
[11] Alexander J. Smola,et al. Advances in Large Margin Classifiers , 2000 .
[12] Kazushi Ikeda,et al. Effects of kernel function on Nu support vector machines in extreme cases , 2006, IEEE Transactions on Neural Networks.
[13] Kristin P. Bennett,et al. Duality and Geometry in SVM Classifiers , 2000, ICML.
[14] Kazushi Ikeda. An Asymptotic Statistical Theory of Polynomial Kernel Methods , 2004, Neural Computation.
[15] Noboru Murata,et al. Support vector machines with different norms: motivation, formulations and results , 2001, Pattern Recognit. Lett..
[16] Kazushi Ikeda,et al. An asymptotic statistical analysis of support vector machines with soft margins , 2005, Neural Networks.
[17] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[18] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[19] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.