Presentation | 1996/5/24 Generalization Error of a Linear Neural Network with a Singular Fisher Information Matrix Kenji Fukumizu, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We calculate the generalization error of a special linear neural network whose information matrix is singular at the true parameter. It is well known that the information matrix in certain non-nonlinear models can be singular. Many statistical methods are not applicable to such cases, because they essentially depend on the ordinary asymptotic theory which assumes the regularity of the information matrix. Thus, it is very important to clarify the generalization error in the singular cases. In this paper, we discuss a special model of linear neural networks, and show that the maximum likelihood estimator of this model is rigorously solvable. We calculate the generalization error in some special cases where the information matrix is singular at the true parameter, and show that the generalization error is larger than the error of a network with a regular information matrix. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Linear neural network / Maximum likelihood estimation / Generalization error / Information matrix. |
Paper # | NC96-3 |
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Conference Information | |
Committee | NC |
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Conference Date | 1996/5/24(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Generalization Error of a Linear Neural Network with a Singular Fisher Information Matrix |
Sub Title (in English) | |
Keyword(1) | Linear neural network |
Keyword(2) | Maximum likelihood estimation |
Keyword(3) | Generalization error |
Keyword(4) | Information matrix. |
1st Author's Name | Kenji Fukumizu |
1st Author's Affiliation | Information and Communication R & D Center, RlCOH Co., Ltd.() |
Date | 1996/5/24 |
Paper # | NC96-3 |
Volume (vol) | vol.96 |
Number (no) | 76 |
Page | pp.pp.- |
#Pages | 8 |
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