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Rodríguez, Juan Tinguaro and Montero, Javier and Vitoriano, Begoña (2012) Dissimilarity-based bipolar superbised classfication. In Uncertainty Modeling in Knowledge Engineering and Decision Making. World Scientific Proceedings Series on Computer Engineering and Information Science (7). World Scientific, Hackensack, pp. 894-899. ISBN 978-981-4417-74-7
Official URL: http://www.worldscientific.com/doi/abs/10.1142/9789814417747_0143
Abstract
Frequently, the set of classes of a supervised classification problem presents an structure related to the specific features of each application context. However, standard classification models does not use to consider such an structure in their learning and reasoning processes. By means of the introduction of a bipolar approach, this paper proposes a revision of the basic notions of supervised classifiers, aimed to extend their generalization power and adaptation to problems with an structured set of classes.
| Item Type: | Book Section |
|---|---|
| Additional Information: | PART 4. STATISTICS, DATA ANALYSIS AND DATA MINING |
| Subjects: | Sciences > Mathematics > Operations research |
| ID Code: | 29622 |
| Deposited On: | 17 Apr 2015 07:56 |
| Last Modified: | 25 May 2016 15:19 |
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