Method and device for creating a system for the automated creation of machine learning systems

A non-transitory machine-readable memory medium on which is stored a computer program for creating a system, which is suitable for creating, in an automated manner, a machine learning system for computer vision, the computer program, when executed by a computer, causing the computer to perform the following steps:

• providing predefined hyperparameters, the hyperparameters including at least one first parameter, which characterizes which optimization method is used, and one second parameter, which characterizes of what type of the machine learning system is;
• determining an optimal parameterization of the hyperparameters using BOHB (Bayesian optimization (BO) and Hyperband (HB)) for each single training data set of a plurality of different training data sets for computer vision;
• assessing all of the optimal parameterizations on all training data sets of the plurality of different training data sets using a normalized metric;
• creating a matrix, the matrix including the normalized metric for each of the optimal parameterizations and for each of the training data sets;
• determining meta-features for each of the training data sets, the meta-features characterizing at least one of the following properties of the training data sets:
• image resolution, number of classes, number of training points/test data points, number of video frames;
• initializing a system including a decision tree;
• and
• optimizing the decision tree as a function of the meta-features and of the matrix, so that the decision tree outputs which of the determined optimal parameterizations using BOHB is a suitable parameterization for the meta-features.

A device configured to create a system, which is suitable for creating, in an automated manner, a machine learning system for computer vision, the method comprising the following steps:

• providing predefined hyperparameters, the hyperparameters including at least one first parameter, which characterizes which optimization method is used, and one second parameter, which characterizes of what type of the machine learning system is;
• determining an optimal parameterization of the hyperparameters using BOHB (Bayesian optimization (BO) and Hyperband (HB)) for each single training data set of a plurality of different training data sets for computer vision;
• assessing all of the optimal parameterizations on all training data sets of the plurality of different training data sets using a normalized metric;
• creating a matrix, the matrix including the normalized metric for each of the optimal parameterizations and for each of the training data sets;
• determining meta-features for each of the training data sets, the meta-features characterizing at least one of the following properties of the training data sets:
• image resolution, number of classes, number of training points/test data points, number of video frames;
• initializing a system including a decision tree;
• and
• optimizing the decision tree as a function of the meta-features and of the matrix, so that the decision tree outputs which of the determined optimal parameterizations using BOHB is a suitable parameterization for the meta-features.