Oct 4, 2018 · The principal intent of this paper is to introduce a PCA-based optimal FSS for fuzzy extreme learning machine (PF-FELM) approach that is able to ...
With the results, it is inferred that PF-FELM provides 12.17% improved generalization perfor- mance as compared to P-FELM (without FSS) by reducing 13.94%.
The principal intent of this paper is to introduce a PCA-based optimal FSS for fuzzy extreme learning machine (PF-FELM) approach that is able to handle ...
The principal intent of this paper is to introduce a PCA based optimal FSS for fuzzy extreme learning machine (PF-FELM) approach which is able to handle ...
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May 29, 2023 · Kale A P and Sonavane S. (2018) PF-FELM: A robust PCA feature selection for fuzzy extreme learning machine. IEEE Journal of Selected Topics in ...
PF-FELM: A robust PCA feature selection for fuzzy extreme learning machine. AP Kale, S Sonavane. IEEE Journal of Selected Topics in Signal Processing 12 (6) ...
PF-FELM: A Robust PCA Feature Selection for Fuzzy Extreme Learning Machine · A. KaleS. Sonavane. Computer Science. IEEE Journal of Selected Topics in Signal…
Kale and S. Sonavane, "PF-FELM: A Robust PCA Feature Selection for Fuzzy. Extreme Learning Machine," in IEEE Journal of Selected Topics in Signal Processing ...
PF-FELM: A Robust PCA Feature Selection for Fuzzy Extreme Learning Machine. IEEE Journal of Selected Topics in Signal Processing. 2018-12 | Journal article.
Oct 22, 2024 · PF-FELM : A Robust PCA Feature Selection for Fuzzy Extreme Learning Machine. Article. Oct 2018. Archana Kale · Shefali Sonavane.