open access

https://hdl.handle.net/2115/49873

Theoretical analyses for a class of kernels with an invariant metric

英語

metric

2010-03

IEEE

2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)

2074-2077

One of central topics of kernel machines in the field of machine learning is a model selection, especially a selection of a kernel or its parameters. In our previous work, we discussed a class of kernels whose corresponding reproducing kernel Hilbert spaces have an invariant metric and proved that the kernel corresponding to the smallest reproducing kernel Hilbert space, including an unknown true function, gives the optimal model. However, discussions for properties that make the metrics of reproducing kernel Hilbert spaces invariant are insufficient. In this paper, we show a necessary and sufficient condition that makes the metrics of reproducing kernel Hilbert spaces invariant.

会議発表論文

Accepted Manuscript

© 2010 IEEE. Reprinted, with permission, from Tanaka, A.; Miyakoshi, M., Theoretical analyses for a class of kernels with an invariant metric, 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), March 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.

1520-6149