HUSCAP logo Hokkaido Univ. logo

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?

Files in This Item:
e98-a_11_2315.pdf549.28 kBPDFView/Open
Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/60358

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

Export metadata:

OAI-PMH ( junii2 , jpcoar_1.0 )

MathJax is now OFF:


 

 - Hokkaido University