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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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
A generative learning model of rational inattention is introduced for travel choice, shown to correlate with the theory and reformulated as a generalized entropy-utility multinomial logit.
citing papers explorer
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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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Information processing constraints in travel behaviour modelling: A generative learning approach
A generative learning model of rational inattention is introduced for travel choice, shown to correlate with the theory and reformulated as a generalized entropy-utility multinomial logit.