A method for identifying a set of interpolation point data points from training data for a sparse Gaussian process model, encompassing the following tasks: successively selecting training data points from the set of training data for acceptance into or exclusion from a set of interpolation point data points in accordance with a selection criterion; and terminating selection when a termination criterion exists; the selection criterion depending on a divergence between a target value of the selected training data point and a function value, at the selected training data point, of the Gaussian process model based on the respectively current set of interpolation point data points.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for identifying a set of interpolation point data points from training data for a sparse Gaussian process model, the method comprising: successively selecting training data points from the set of training data for acceptance into or exclusion from a set of interpolation point data points in accordance with a selection criterion; and terminating selection when a termination criterion exists; wherein the selection criterion depends on a divergence between a target value of the training data point to be selected and a function value, at the training data point to be selected, of the Gaussian process model based on the respectively current set of interpolation point data points. 2. The method of claim 1, wherein the successive selection of the training data points from the set of training data for acceptance into the set of interpolation point data points in accordance with the selection criterion encompassing the fact that in each selection cycle the selection criterion identifies for acceptance into the set of interpolation point data points that training data point whose target value maximally diverges from the function value of the Gaussian process model defined by the previously determined interpolation point data. 3. The method of claim 2, wherein the termination criterion encompass ascertaining that a defined number of selected interpolation point data points has been reached, and/or ascertaining that the maximum divergence falls below a defined error threshold. 4. The method of claim 2, wherein determination of the set of interpolation point data points is carried out iteratively, such that after each determination of the set of interpolation point data points, hyperparameters based thereon are determined for the sparse Gaussian process model and the set of interpolation point data points is determined again, the iterative determination being carried out as long as an iteration criterion is met. 5. The method of claim 4, wherein the iteration criterion is met if the average divergence of the target values of the set of interpolation point data from the function values of the determined sparse Gaussian process model falls below a defined limit value. 6. The method of claim 2, wherein the hyperparameters for the sparse Gaussian process model are identified after determination of the set of interpolation point data. 7. The method of claim 1, wherein the successive selection of training data points from the set of training data for exclusion from the set of interpolation point data points in accordance with the selection criterion encompasses the fact that in each selection cycle the selection criterion identifies, for exclusion from the set of interpolation point data points, that training data point whose target value diverges minimally from the function value of the Gaussian process model defined by the previously determined interpolation point data. 8. The method of claim 2, wherein the termination criterion encompasses ascertaining that a defined number of selected interpolation point data points has been reached, and/or ascertaining that the minimal divergence exceeds a defined error threshold value. 9. An apparatus for identifying from training data a set of interpolation point data points for a sparse Gaussian process model, comprising: a processor arrangement configured to perform the following: successively selecting from the set of training data, in accordance with a selection criterion, training data points for acceptance into or exclusion from a set of interpolation point data points; and terminating selection when a termination criterion exists; wherein the selection criterion depends on a divergence between a target value of the training data point to be selected and a function value, at the training data point to be selected, of the Gaussian process model based on the respectively current set of interpolation point data points. 10. A system, comprising: an apparatus for identifying from training data a set of interpolation point data points for a sparse Gaussian process model, including: a processor arrangement configured to perform the following: successively selecting from the set of training data, in accordance with a selection criterion, training data points for acceptance into or exclusion from a set of interpolation point data points; and terminating selection when a termination criterion exists; wherein the selection criterion depends on a divergence between a target value of the training data point to be selected and a function value, at the training data point to be selected, of the Gaussian process model based on the respectively current set of interpolation point data points; and a control device configured to receive the interpolation point data and calculate the sparse Gaussian process model. 11. A computer readable medium having a computer program, which is executable by a processor, comprising: a program code arrangement having program code for identifying from training data a set of interpolation point data points for a sparse Gaussian process model, by performing the following: successively selecting from the set of training data, in accordance with a selection criterion, training data points for acceptance into or exclusion from a set of interpolation point data points; and terminating selection when a termination criterion exists; wherein the selection criterion depends on a divergence between a target value of the training data point to be selected and a function value, at the training data point to be selected, of the Gaussian process model based on the respectively current set of interpolation point data points. 12. The computer readable medium of claim 11, wherein the successive selection of the training data points from the set of training data for acceptance into the set of interpolation point data points in accordance with the selection criterion encompassing the fact that in each selection cycle the selection criterion identifies for acceptance into the set of interpolation point data points that training data point whose target value maximally diverges from the function value of the Gaussian process model defined by the previously determined interpolation point data. 13. An electronic control device, comprising: a computer readable medium having a computer program, which is executable by a processor, including a program code arrangement having program code for identifying from training data a set of interpolation point data points for a sparse Gaussian process model, by performing the following: successively selecting from the set of training data, in accordance with a selection criterion, training data points for acceptance into or exclusion from a set of interpolation point data points; and terminating selection when a termination criterion exists; wherein the selection criterion depends on a divergence between a target value of the training data point to be selected and a function value, at the training data point to be selected, of the Gaussian process model based on the respectively current set of interpolation point data points.