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Noise Benefits in Feedback Machine Learning: Bidirectional Backpropagation

  • Bart KoskoEmail author
Conference paper
Part of the Understanding Complex Systems book series (UCS)

Abstract

The new bidirectional backpropagation algorithm converts an ordinary feedforward neural network into a simple feedback dynamical system. The algorithm minimizes a joint performance measure so that training in one direction does not overwrite training in the reverse direction. This involves little extra computation. The forward direction gives the usual classification or regression network. The new backward pass approximates the centroids of the input pattern classes in a neural classifier. The bidirectional algorithm can also approximate inverse point mappings in the rare cases where such mappings exist. Carefully injected noise can speed the convergence of the bidirectional backpropagation. This holds because backpropagation is a special case of the expectation-maximization algorithm for maximum likelihood and because such noise can always boost its convergence. The noise also tends to improve accuracy in classification and regression.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Electrical and Computer Engineering DepartmentSignal and Image Processing Institute, University of Southern CaliforniaLos AngelesUSA

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