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Augmenting Deep Learning Performance in an Evidential Multiple Classifier System
2019
Sensors
The main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier's respective strengths, we combine them in an evidential framework, which models
doi:10.3390/s19214664
pmid:31717870
pmcid:PMC6864766
fatcat:hnwjfpvnpbh6rptt62zoe4ekdi