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Scalable Variable Selection for Reproducing Kernel Hilbert Spaces Methods
| Full text | |
| Author(s): |
Guevara, Jorge
;
Mendel, Jerry M.
;
Hirata, Roberto
Total Authors: 3
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON FUZZY SYSTEMS; v. 30, n. 10, p. 13-pg., 2022-10-01. |
| Abstract | |
This article introduces the fuzzy-system kernel machines-a class of machine learning models based on the connection between fuzzy inference systems and kernel machines. For the connection, we observed a relationship between the representer theorem of kernel methods and the functional representation of nonsingleton fuzzy systems. We found that the nonsingleton kernel on fuzzy sets-a kernel defined in this article-is the core element allowing this two-way connection perspective. Consequently, a fuzzy system trained with the kernel method can be regarded as a kernel machine, whereas a kernel machine trained with a nonsingleton kernel on fuzzy sets can be interpreted as a fuzzy system. We conducted several experiments in supervised classification to understand the generalization power and properties of the proposed fuzzy-system kernel machines. (AU) | |
| FAPESP's process: | 14/50937-1 - INCT 2014: on the Internet of the Future |
| Grantee: | Fabio Kon |
| Support Opportunities: | Research Projects - Thematic Grants |