Abstract: A computer-implemented method for training an artificial neural network using training data, which include features and identifiers, the features characterizing term candidates from a corpus, the corpus including a text from a domain, the identifier characterizing a degree of association to at least three classes for term candidates that differ from one another, different classes indicating different degrees of association of the term candidates to the domain, the training data including an assignment of features to identifiers. An artificial neural network, method for classifying term candidates, and computer-implemented method for generating training data, are also described.

Abstract: A method in which user information is transmitted from at least one data source to a processing unit of a learning device. The user information is used, by the processing unit via a machine learner, to generate at least one user model. The at least one user model is adapted via an adaptation of parameters used by the at least one machine learner to generating the at least one user model. The parameters, used by the at least one machine learner for generating the at least one user model, are adapted as a function of at least one predefined rule. The user model generated on the basis of the adapted parameters is used to personalize at least one terminal.

Abstract: A computer-implemented method for training an artificial neural network using training data, which include features and identifiers, the features characterizing term candidates from a corpus, the corpus including a text from a domain, the identifier characterizing a degree of association to at least three classes for term candidates that differ from one another, different classes indicating different degrees of association of the term candidates to the domain, the training data including an assignment of features to identifiers. An artificial neural network, method for classifying term candidates, and computer-implemented method for generating training data, are also described.

Abstract: A method for controlling user information in an automatically learning device, user information being transmitted from at least one data source to a processing unit of the learning device and the user information being used by the processing unit for generating at least one user model using at least one machine learner, the at least one user model being adapted via an adaptation of parameters, which are used by the at least one machine learner for generating the at least one user model, and the parameters, which are used by the at least one machine learner for generating the at least one user model are adapted as a function of at least one predefined rule, and the user model generated on the basis of the adapted parameters being used to personalize at least one terminal.

Abstract: A driver information device includes an output unit for outputting items of driver information, a pause having a predetermined length being inserted between the outputting of a first and a second item of information.

Abstract: An apparatus is described for assisting driving of a vehicle having at least two driver assistance systems and having an output device or element for communicating information items of at least one of the driver assistance systems to a driver of the vehicle. To relieve the strain on the driver, a control device or element, configured as an information coordinator and connected at its input side to the driver assistance systems and at its output side to the output device is provided for controlling information items of the driver assistance systems to be communicated to the driver.

Abstract: A driver information device includes an output unit for outputting items of driver information, a pause having a predetermined length being inserted between the outputting of a first and a second item of information.