Presentation | 2015-03-06 Model selection with approximate validation error guarantee for L^2_2 regularized convex loss minimization problems Atsushi SHIBAGAKI, Yoshiki SUZUKI, Ichiro TAKEUCHI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper we propose a new algorithm that can select an approximately optimal regularization parameter in a class of regularized convex classifier learning problems. The selected regularization parameter has a theoretical approximation guarantee in the sense that the validation error for the regularization parameter is at most greater by e than the smallest possible validation error, where ε∈ [0, 1] is a user specified tolerance. We demonstrate the effectiveness of the proposed method through numerical experiments. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Machine Learning / Model Selection / Approximate Regularization Path / Convex Optimization |
Paper # | IBISML2014-96 |
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Committee | IBISML |
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Conference Date | 2015/2/26(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Model selection with approximate validation error guarantee for L^2_2 regularized convex loss minimization problems |
Sub Title (in English) | |
Keyword(1) | Machine Learning |
Keyword(2) | Model Selection |
Keyword(3) | Approximate Regularization Path |
Keyword(4) | Convex Optimization |
1st Author's Name | Atsushi SHIBAGAKI |
1st Author's Affiliation | Nagoya Institute of Technology() |
2nd Author's Name | Yoshiki SUZUKI |
2nd Author's Affiliation | Nagoya Institute of Technology |
3rd Author's Name | Ichiro TAKEUCHI |
3rd Author's Affiliation | Nagoya Institute of Technology |
Date | 2015-03-06 |
Paper # | IBISML2014-96 |
Volume (vol) | vol.114 |
Number (no) | 502 |
Page | pp.pp.- |
#Pages | 8 |
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