Abstract: The invention relates to a system which, on the one hand, has a classifier that is formed by a discriminative neural network and that implements a binary class model or a multi-class model. On the other hand, the system has a model-based sample generator that is formed by a generative neural network. Both the classifier and the model-based sample generator are trained—for a corresponding class—with the same training data records and therefore embody models that correspond to one another for this class. The invention also relates to a method for determining a quality criterion for input data records for a classifier with a discriminative neural network. The classifier has been trained with training data records and represents a classification model for a class. According to the method, a model-based sample generator with a generative neural network is initially provided and trained with the same training data records that were used to train the classifier.

Abstract: A system for automated harmonization of structured data from acquisition devices, comprising an input for input data sets in different, acquisition device-specific data structures, a harmonization module embodying a harmonization model configured to transform a respective input data set from the respective system acquisition device-specific structure into at least one harmonized data set in a globally uniform, harmonized data structure of the system, a preprocessing module embodying a preprocessing model configured to transform data from a harmonized data set into data in a model-specific data structure, in particular to perform feature reduction so that a data set with preprocessed data in the model-specific data structure represents fewer features than a corresponding data set in the globally uniform structure, and an automated processing facility configured to automatically process preprocessed data in the model-specific data structure to classify and to generate a loss measure representing a possible p

Abstract: The invention relates to a system which, on the one hand, has a classifier that is formed by a discriminative neural network and that implements a binary class model or a multi-class model. On the other hand, the system has a model-based sample generator that is formed by a generative neural network. Both the classifier and the model-based sample generator are trained—for a corresponding class—with the same training data records and therefore embody models that correspond to one another for this class. The invention also relates to a method for determining a quality criterion for input data records for a classifier with a discriminative neural network. The classifier has been trained with training data records and represents a classification model for a class. According to the method, a model-based sample generator with a generative neural network is initially provided and trained with the same training data records that were used to train the classifier.

Abstract: The invention relates to a classifier system for classifying states of a system that is characterized by measurable system parameters, or for classifying objects, which classifier system has a plurality of decentralized, i.e. local, classifier units and a central classifier unit. The decentralized classifier units can be clients, for example, and the central classifier unit can be a server in a client-server system. In this type of system, the decentralized classifier units are formed (trained) in a decentralized manner and subsequently combined centrally to form binary classifier units which, in turn, can form a multiclass classifier unit.