Method and device for continual machine learning of a sequence of different tasks

Abstract

A method for parameterizing a function, which outputs an ideal parameterization of a machine learning system for a large number of different data sets. A first training of a machine learning system is carried out in succession on multiple training data sets, the individual optimized parameterizations of the machine learning system being stored for each of the training data sets. A second training of the machine learning system simultaneously on all data sets then follows, the optimal parameterization of the machine learning system being stored. An optimization of the parameterization of the function thereupon follows in such a way that, given an optimal parameterization of the first training, the function outputs the associated optimal parameterization of the second training.