Model Selection Using a Class of Kernels with an Invariant Metric
Learning based on kernel machines is widely known as a powerful tool for various fields of information science such as pattern recognition and regression estimation. The efficacy of the model in kernel machines depends on the distance between the unknown true function and the linear subspace, specified by the training data set, of the reproducing kernel Hilbert space corresponding to an adopted kernel. In this paper, we propose a framework for the model selection of kernel-based learning machines, incorporating a class of kernels with an invariant metric.
KeywordsTrue Function Reproduce Kernel Hilbert Space Kernel Machine Parametric Projection Machine Learning Problem
Unable to display preview. Download preview PDF.
- 2.Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1999)Google Scholar
- 3.Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Recognition. Cambridge University Press, Cambridge (2004)Google Scholar
- 4.Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)Google Scholar
- 7.Ogawa, H.: Neural Networks and Generalization Ability. IEICE Technical Report NC95-8, 57–64 (1995)Google Scholar
- 10.Imai, H., Tanaka, A., Miyakoshi, M.: The family of parametric projection filters and its properties for perturbation. The IEICE Transactions on Information and Systems E80–D, 788–794 (1997)Google Scholar