Stacked Training for Overfitting Avoidance in Deep Networks
نویسندگان
چکیده
When training deep networks and other complex networks of predictors, the risk of overfitting is typically of large concern. We examine the use of stacking, a method for training multiple simultaneous predictors in order to simulate the overfitting in early layers of a network, and show how to utilize this approach for both forward training and backpropagation learning in deep networks. We then compare this approach to overfitting avoidance with the dropout method for a number of common tasks.
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