Presentation | 2012-11-08 Efficient SVM Bootstrap Computation by Parametric Programming Yoshiki SUZUKI, Kohei OGAWA, Ichiro TAKEUCHI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper, we study statistical variability estimation of the support vector machine (SVM) by using bootstrap method. Training a large number of the SVMs for each of the thousands of bootstrap samples is quite time-consuming. We therefore develop a new algorithm for efficiently computing these large set of the SVMs by introducing two techniques called parametric programming and sphere test. Parametric programming allows us to efficiently compute the optimal solutions of two similar problems, while sphere test enables us to estimate the similarity or distance between two different SVMs. We demonstrate the effectiveness of our algorithm through numerical experiments on real data. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Support Vector Machine / Bootstrap / Parametric Programing / Sphere Test |
Paper # | IBISML2012-73 |
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Committee | IBISML |
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Conference Date | 2012/10/31(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) | Efficient SVM Bootstrap Computation by Parametric Programming |
Sub Title (in English) | |
Keyword(1) | Support Vector Machine |
Keyword(2) | Bootstrap |
Keyword(3) | Parametric Programing |
Keyword(4) | Sphere Test |
1st Author's Name | Yoshiki SUZUKI |
1st Author's Affiliation | Department of Engineering, Nagoya Institute of Technology() |
2nd Author's Name | Kohei OGAWA |
2nd Author's Affiliation | Department of Engineering, Nagoya Institute of Technology |
3rd Author's Name | Ichiro TAKEUCHI |
3rd Author's Affiliation | Department of Engineering, Nagoya Institute of Technology |
Date | 2012-11-08 |
Paper # | IBISML2012-73 |
Volume (vol) | vol.112 |
Number (no) | 279 |
Page | pp.pp.- |
#Pages | 6 |
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