Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images
PeerJ2019Vol. 7, pp. e6977–e6977
Citations Over TimeTop 10% of 2019 papers
Abstract
Ensemble learning reduces the model variance by optimally combining the predictions of multiple models and decreases the sensitivity to the specifics of training data and selection of training algorithms. The performance of the model ensemble simulates real-world conditions with reduced variance, overfitting and leads to improved generalization.
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