open access

https://hdl.handle.net/2115/46851

A Relationship Between Generalization Error and Training Samples in Kernel Regressors

英語

sample points

2010-08-23

IEEE

2010 20th International Conference on Pattern Recognition (ICPR)

1421-1424

A relationship between generalization error and training samples in kernel regressors is discussed in this paper. The generalization error can be decomposed into two components. One is a distance between an unknown true function and an adopted model space. The other is a distance between an estimated function and the orthogonal projection of the unknown true function onto the model space. In our previous work, we gave a framework to evaluate the first component. In this paper, we theoretically analyze the second one and show that a larger set of training samples usually causes a larger generalization error.

会議発表論文

Accepted Manuscript

© 2010 IEEE. Reprinted, with permission, from Akira Tanaka, Hideyuki Imai, Mineichi Kudo, and Masaaki Miyakoshi, A Relationship Between Generalization Error and Training Samples in Kernel Regressors, 2010 International Conference on Pattern Recognition, Aug. 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.