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Extensive semi-quantitative regression
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DOI：10.1016/j.neucom.2016.08.073.png)
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摘要
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In this paper, we propose and solve a new machine learning problem called the extensive semi-quantitative regression, where the information about some target values is incomplete; we only know their lower bounds and/or upper bounds instead of their exact values. To employ the information efficiently in extensive semi-quantitative regression, we introduce a local graph to capture the geometric structure for the samples with the exact target values and the target bounds, and construct a graph-based support vector regressor, called ESQ-SVR. The efficiency of our ESQ-SVR is supported by the results of preliminary experiments conducted on both the artificial and real world datasets. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Machine learning
Regression
Extensive semi-quantitative regression
Support vector machines
Laplacian graph
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6.5
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2.5W
被引数:
6.5W
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