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• W3011690198 abstract "In this paper, we propose regular vine copula based fusion of multiple deep neural network classifiers for the problem of multi-sensor based human activity recognition. We take the cross-modal dependence into account by employing regular vine copulas that are extremely flexible and powerful graphical models to characterize complex dependence among multiple modalities. Multiple deep neural networks are used to extract high-level features from multi-sensing modalities, with each deep neural network processing the data collected from a single sensor. The extracted high-level features are then combined using a regular vine copula model. Numerical experiments are conducted to demonstrate the effectiveness of our approach." @default.
• W3011690198 created "2020-03-23" @default.
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• W3011690198 date "2019-05-07" @default.
• W3011690198 modified "2023-09-23" @default.
• W3011690198 title "Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach" @default.
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• W3011690198 doi "https://doi.org/10.48550/arxiv.1905.02703" @default.
• W3011690198 hasPublicationYear "2019" @default.
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