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Theoretical Analyses on 2-Norm-Based Multiple Kernel Regressors

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/65520

Title: Theoretical Analyses on 2-Norm-Based Multiple Kernel Regressors
Authors: Tanaka, Akira Browse this author →KAKEN DB
Imai, Hideyuki Browse this author →KAKEN DB
Keywords: multiple kernel regressor
reproducing kernel Hilbert space
generalization error
2-norm criterion
2-norm regularizer
Issue Date: Mar-2017
Publisher: 電子情報通信学会
Journal Title: IEICE transactions on fundamentals of electronics communications and computer sciences
Volume: E100A
Issue: 3
Start Page: 877
End Page: 887
Publisher DOI: 10.1587/transfun.E100.A.877
Abstract: The solution of the standard 2-norm-based multiple kernel regression problem and the theoretical limit of the considered model space are discussed in this paper. We prove that 1) The solution of the 2-norm-based multiple kernel regressor constructed by a given training data set does not generally attain the theoretical limit of the considered model space in terms of the generalization errors, even if the training data set is noise-free, 2) The solution of the 2-norm-based multiple kernel regressor is identical to the solution of the single kernel regressor under a noise free setting, in which the adopted single kernel is the sum of the same kernels used in the multiple kernel regressor; and it is also true for a noisy setting with the 2-norm-based regularizer. The first result motivates us to develop a novel framework for the multiple kernel regression problems which yields a better solution close to the theoretical limit, and the second result implies that it is enough to use the single kernel regressors with the sum of given multiple kernels instead of the multiple kernel regressors as long as the 2-norm based criterion is used.
Rights: copyright©2017 IEICE
Relation: http://search.ieice.org/
Type: article
URI: http://hdl.handle.net/2115/65520
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

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