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Proceedings Paper

Kernel credal classification rule
Author(s): Khawla El Bendadi; Yissam Lakhdar; El Hassan Sbai
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Paper Abstract

In this paper, we propose a kernel version of the credal classification rule (CCR) to perform the classification in a feature space of high dimension. Kernels based approaches have become popular for several years to solve supervised or unsupervised learning problems. In this paper, our method is extended to the CCR. It is realized by replacing the inner product with an appropriate positive definite function, and the corresponding algorithms are called kernel Credal Classification Rule (KCCR). The approach is applied to the classification of the generated and real data to evaluate and compare the performance of the KCCR method with other classification methods.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103412I (17 March 2017); doi: 10.1117/12.2268730
Show Author Affiliations
Khawla El Bendadi, Univ. Moulay Ismail (Morocco)
Yissam Lakhdar, Univ. Moulay Ismail (Morocco)
El Hassan Sbai, Univ. Moulay Ismail (Morocco)


Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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