Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4948133 | Neurocomputing | 2016 | 11 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yuan-Hai Shao, Ya-Fen Ye, Yong-Cui Wang, Nai-Yang Deng,