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Deep learning is the state-of-the-art in fields such as visual objectrecognition and speech recognition. This learning uses a large number oflayers, huge number of units, and connections. Therefore, overfitting is aserious problem. To avoid this problem, dropout learning is proposed. Dropoutlearning neglects some inputs and hidden units in the learning process with aprobability, p, and then, the neglected inputs and hidden units are combinedwith the learned network to express the final output. We find that the processof combining the neglected hidden units with the learned network can beregarded as ensemble learning, so we analyze dropout learning from this pointof view.

Analysis of dropout learning regarded as ensemble learning