This thesis describes the integration of the sparse grid density estimation-based
classification into the datamining pipeline of the SG++ toolbox. As the sparse grid density …

In this thesis a new algorithm for adaptive sparse grid density estimation is introduced and
analyzed. In an offline/online splitting context an orthogonal decomposition of the underlying …

Employing sparse grids for data mining mitigates the curse of dimensionality, is applicable
for mining large datasets, and offers an explainable approach to machine learning. In this …

This thesis describes cluster-level domain-parallelization of a sparse grid density estimation
(SGDE) based classification algorithm. The Online phase of the implementation of this …

In this bachelor thesis, a method is introduced to improve the offline step of sparse grid
density estimation with the combination technique. The developed approach exploits the …

Sherman-Morrison rank-one updates have been used successfully for adaptive sparse grid
density estimation. This allowed for regularization and adaptivity, but until now, this has only …

In higher dimensional interpolation and quadrature problems, sparse grid approaches have
already shown to be a good alternative to classical full grid approximation schemes which …