Abstract
We introduce a new method to combine the output probabilities of convolutional neural networks which we call Weighted Convolutional Neural Network Ensemble. Each network has an associated weight that makes networks with better performance have a greater influence at the time to classify in relation to networks that performed worse. This new approach produces better results than the common method that combines the networks doing just the average of the output probabilities to make the predictions. We show the validity of our proposal by improving the classification rate on a common image classification benchmark.
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Frazão, X., Alexandre, L.A. (2014). Weighted Convolutional Neural Network Ensemble. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_82
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DOI: https://doi.org/10.1007/978-3-319-12568-8_82
Publisher Name: Springer, Cham
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