The authors introduce dRVFL and edRVFL frameworks that stack RVFL layers with fixed random weights and closed-form outputs, reporting superior benchmark performance when combined with sparse-pretrained RVFL.
Demˇ sar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine learning research 7 (Jan) (2006) 1–30
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
RKNN-TSVM enhances KNN-based twin SVM by weighting samples via neighbor distances, adding stabilizer terms for stable learning, and embedding LDMDBA for faster KNN computation, with experiments claiming higher accuracy and up to 14x speedup.
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Random Vector Functional Link Neural Network based Ensemble Deep Learning
The authors introduce dRVFL and edRVFL frameworks that stack RVFL layers with fixed random weights and closed-form outputs, reporting superior benchmark performance when combined with sparse-pretrained RVFL.
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An enhanced KNN-based twin support vector machine with stable learning rules
RKNN-TSVM enhances KNN-based twin SVM by weighting samples via neighbor distances, adding stabilizer terms for stable learning, and embedding LDMDBA for faster KNN computation, with experiments claiming higher accuracy and up to 14x speedup.