Data mining with sparse grids
| Author(s) : | M. Thess M. Griebel J. Garcke, |
| Publisher : | N/A |
| Publication Date : | 2001 |
| ISSN : | N/A |
| Abstract : | We present a new approach to the classification problem arising in data mining. It is based on the regularization network approach but, in contrast to the other methods which employ ansatz functions associated to data points, we use a grid in the usually high-dimensional feature space for the minimization process. To cope with the curse of dimensionality, we employ sparse grids. Thus, only O(h \Gamma1, |