NEURAL NETWORK LEARNING METHOD AND APPARATUS USING UNSUPERVISED LEARNING BASED ON ARCHITECTURE VARIATION AND SUPERVISED LEARNING BASED ON SELECTIVE ERROR PROPAGATION

摘要

According to the present invention, disclosed is a neural network learning method which includes the steps of: generating a set of candidate solutions by transforming a candidate solution representing a basic neural network model in the form of a variable-length string; obtaining a first candidate solution by performing architecture variation based unsupervised learning on a plurality of candidate solutions selected from the set of candidate solutions; selecting a neural network model represented by the first candidate solution that satisfies target effective performance as a first neural network model; obtaining a second candidate solution by performing selective error propagation based supervised learning on the first neural network model; and selecting a neural network model represented by the second candidate solution that satisfies the target effective performance as a final neural network model.;COPYRIGHT KIPO 2020