Presentation | 2012-11-08 Local Semi-supervised Gaussian Process Regression based-on Clustering Xinlu GUO, Yoshiaki YASUMURA, Kuniaki UEHARA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The majority of the existing graph-based semi-supervised learning algorithms have been applied to the classification task. In this paper we propose a graph-based semi-supervised Gaussian process (GP) algorithm for solving regression problem. Our method incorporates an adjacent graph, which is built on labeled and unlabeled data, with the standard GP prior to infer the new training and predicting distribution for semi-supervised GP regression (GPr). Furthermore, we extend our semi-supervised method to a clustering framework for reducing the computational cost in GP. Experimental results show that our work achieves encouraging results. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Semi-supervised Learning / Regression / Gaussian process / Graph Laplacian / Clustering |
Paper # | IBISML2012-86 |
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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) | Local Semi-supervised Gaussian Process Regression based-on Clustering |
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Keyword(1) | Semi-supervised Learning |
Keyword(2) | Regression |
Keyword(3) | Gaussian process |
Keyword(4) | Graph Laplacian |
Keyword(5) | Clustering |
1st Author's Name | Xinlu GUO |
1st Author's Affiliation | Graduate School of System Informatics, Kobe University() |
2nd Author's Name | Yoshiaki YASUMURA |
2nd Author's Affiliation | College of Engineering, Shibaura Institute of Technology |
3rd Author's Name | Kuniaki UEHARA |
3rd Author's Affiliation | Graduate School of System Informatics, Kobe University |
Date | 2012-11-08 |
Paper # | IBISML2012-86 |
Volume (vol) | vol.112 |
Number (no) | 279 |
Page | pp.pp.- |
#Pages | 8 |
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