In the frame of this thesis the sparse grid combination technique was used to expand the density estimation based approach to classification in the SG++ framework. Thanks to the independence of the component grids, it was possible to implement much faster dimensional adaptive refinements. While the old implementation had to refit the whole grid after each refinement step, now just the additionally added components have to be fitted. The thesis contains the theoretical background, a description, and an evaluation of the implementation.     «

In the frame of this thesis the sparse grid combination technique was used to expand the density estimation based approach to classification in the SG++ framework. Thanks to the independence of the component grids, it was possible to implement much faster dimensional adaptive refinements. While the old implementation had to refit the whole grid after each refinement step, now just the additionally added components have to be fitted. The thesis contains the theoretical background, a descript...     »