open access

https://hdl.handle.net/2115/46942

Analyses on kernel-specific generalization ability for kernel regressors with training samples

英語

training samples

2010-12-15

IEEE

2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

61-66

Theoretical analyses on generalization error of a model space for kernel regressors with respect to training samples are given in this paper. In general, the distance between an unknown true function and a model space tends to be small with a larger set of training samples. However, it is not clarified that a larger set of training samples achieves a smaller difference at each point of the unknown true function and the orthogonal projection of it onto the model space, compared with a smaller set of training samples. In this paper, we show that the upper bound of the squared difference at each point of these two functions with a larger set of training samples is not larger than that with a smaller set of training samples. We also give some numerical examples to confirm our theoretical result.

会議発表論文

Accepted Manuscript

© 2011 IEEE. Reprinted, with permission, from Tanaka, A., Miyakoshi, M., Analyses on kernel-specific generalization ability for kernel regressors with training samples, 2010 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Dec. 2010. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Hokkaido University products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.