Presentation 2014-11-17
Efficient leave-one-out cross-validation for L2-regularized classifier
Shota OKUMURA, Yoshiki SUZUKI, Kohei OGAWA, Yuki SHINMURA, Ichiro TAKEUCHI,
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Abstract(in English) Leave-one-out cross-validation (LOOCV) is a useful tool for estimating generalization performances of various machine learning algorithms. However, except for some special cases, computing LOOCV error is quite time-consuming because we must train as many models as the number of instances. In this study, we propose an efficient method for LOOCV of L_2-regularized convex binary classification algorithms. The proposed method allows us to make decisions on whether the left-out instance is correctly classified or not before actually solving the optimization problem. The proposed approach can be applicable to wider class of problems than existing approaches and it can also be used as a stopping criterion in the optimization process. We illustrate the advantage of the proposed method based on numerical experiments.
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Keyword(in English) cross-validation / convex optimization / model selection / support vector machine
Paper # IBISML2014-44
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Committee IBISML
Conference Date 2014/11/10(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Efficient leave-one-out cross-validation for L2-regularized classifier
Sub Title (in English)
Keyword(1) cross-validation
Keyword(2) convex optimization
Keyword(3) model selection
Keyword(4) support vector machine
1st Author's Name Shota OKUMURA
1st Author's Affiliation Department of Engineering, Nagoya Institute of Technology()
2nd Author's Name Yoshiki SUZUKI
2nd Author's Affiliation Department of Engineering, Nagoya Institute of Technology
3rd Author's Name Kohei OGAWA
3rd Author's Affiliation Department of Engineering, Nagoya Institute of Technology
4th Author's Name Yuki SHINMURA
4th Author's Affiliation Department of Engineering, Nagoya Institute of Technology
5th Author's Name Ichiro TAKEUCHI
5th Author's Affiliation Department of Engineering, Nagoya Institute of Technology
Date 2014-11-17
Paper # IBISML2014-44
Volume (vol) vol.114
Number (no) 306
Page pp.pp.-
#Pages 8
Date of Issue