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A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification
2016
Scientific Reports
Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm.
doi:10.1038/srep38201
pmid:27905520
pmcid:PMC5131302
fatcat:xfziaow5czcxhn4rc7bpsjivmu