Ensemble and Multiple Kernel Regressors: Which Is Better?

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Author(s)

    • TANAKA Akira
    • Division of Computer Science and Information Technology, Graduate School of Information Science and Technology, Hokkaido University
    • TAKEBAYASHI Hirofumi
    • Division of Computer Science and Information Technology, Graduate School of Information Science and Technology, Hokkaido University
    • TAKIGAWA Ichigaku
    • Division of Computer Science and Information Technology, Graduate School of Information Science and Technology, Hokkaido University
    • IMAI Hideyuki
    • Division of Computer Science and Information Technology, Graduate School of Information Science and Technology, Hokkaido University
    • KUDO Mineichi
    • Division of Computer Science and Information Technology, Graduate School of Information Science and Technology, Hokkaido University

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.

Journal

  • IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E98.A(11), 2315-2324, 2015

    The Institute of Electronics, Information and Communication Engineers

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