Presentation 2010-06-14
Orthogonalization and thresholding method for a nonparametric regression problem
Katsuyuki HAGIWARA,
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Abstract(in English) For a nonparametric regression problem, we proposed a training scheme based on orthogonalization and thresholding, in which a machine is assumed to be a weighted sum of many fixed basis functions. In the basis of the scheme, vectors of basis function outputs are orthogonalized and coefficients of the orthogonalized vectors are estimated instead of weights. The coefficients are set to zero if those are less than predetermined threshold levels which are assigned componentwisely to each coefficient. We then obtain a resulting weight vector by transforming the thresholded coefficients. In this article, we presented theoretically reasonable threshold levels under an assumption of Gaussian additive noise. For a simple situation, we also gave an upper bound of a generalization error of the training scheme. Further, we gave implementations of the training scheme and showed that performances of the proposed training methods on some datasets are comparable to those of alternative methods.
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Keyword(in English) orthogonalization / thresholding / nonparametric regression
Paper # IBISML2010-8
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Committee IBISML
Conference Date 2010/6/7(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Orthogonalization and thresholding method for a nonparametric regression problem
Sub Title (in English)
Keyword(1) orthogonalization
Keyword(2) thresholding
Keyword(3) nonparametric regression
1st Author's Name Katsuyuki HAGIWARA
1st Author's Affiliation Faculty of Education, Mie University()
Date 2010-06-14
Paper # IBISML2010-8
Volume (vol) vol.110
Number (no) 76
Page pp.pp.-
#Pages 8
Date of Issue