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Title
  • en Ensemble and Multiple Kernel Regressors : Which Is Better?
Creator
Accessrights open access
Rights
  • en copyright©2015 IEICE
Subject
  • Other en kernel regression
  • Other en ensemble kernel regressor
  • Other en multiple kernel regressor
  • Other en generalization error
  • Other en reproducing kernel Hilbert spaces
  • NDC 007
Description
  • Abstract en 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.
Publisher en IEICE - The Institute of Electronics, Information and Communication Engineers
Date
    Issued2015-11
Language
  • eng
Resource Type journal article
Version Type VoR
Identifier HDL http://hdl.handle.net/2115/60358
Relation
  • URI http://search.ieice.org/
  • isIdenticalTo DOI https://doi.org/10.1587/transfun.E98.A.2315
Journal
    • PISSN 1745-1337
      • en IEICE transactions on fundamentals of electronics communications and computer sciences
      • Volume NumberE98 Issue Number11 Page Start2315 Page End2324
File
Oaidate 2023-07-26