Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Ensemble and Multiple Kernel Regressors : Which Is Better?
Title: | Ensemble and Multiple Kernel Regressors : Which Is Better? |
Authors: | Tanaka, Akira Browse this author →KAKEN DB | Takebayashi, Hirofumi Browse this author | Takigawa, Ichigaku Browse this author →KAKEN DB | Imai, Hideyuki Browse this author →KAKEN DB | Kudo, Mineichi Browse this author →KAKEN DB |
Keywords: | kernel regression | ensemble kernel regressor | multiple kernel regressor | generalization error | reproducing kernel Hilbert spaces |
Issue Date: | Nov-2015 |
Publisher: | IEICE - The Institute of Electronics, Information and Communication Engineers |
Journal Title: | IEICE transactions on fundamentals of electronics communications and computer sciences |
Volume: | E98A |
Issue: | 11 |
Start Page: | 2315 |
End Page: | 2324 |
Publisher DOI: | 10.1587/transfun.E98.A.2315 |
Abstract: | For the last few decades, learning with multiple kernels, represented by the ensemble kernel regressor and the multiple kernel regressor, has attracted much attention in the field of kernel-based machine learning. Although their efficacy was investigated numerically in many works, their theoretical ground is not investigated sufficiently, since we do not have a theoretical framework to evaluate them. In this paper, we introduce a unified framework for evaluating kernel regressors with multiple kernels. On the basis of the framework, we analyze the generalization errors of the ensemble kernel regressor and the multiple kernel regressor, and give a sufficient condition for the ensemble kernel regressor to outperform the multiple kernel regressor in terms of the generalization error in noise-free case. We also show that each kernel regressor can be better than the other without the sufficient condition by giving examples, which supports the importance of the sufficient condition. |
Rights: | copyright©2015 IEICE |
Relation: | http://search.ieice.org/ |
Type: | article |
URI: | http://hdl.handle.net/2115/60358 |
Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
|
Submitter: 田中 章
|