Information-based complexity
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Computing
Improving RBF-DDA Performance on Optical Character Recognition through Parameter Selection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Regression with the optimised combination technique
ICML '06 Proceedings of the 23rd international conference on Machine learning
Adaptive Sparse Grid Classification Using Grid Environments
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Clustering based on density estimation with sparse grids
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
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Sparse grids allow one to employ grid-based discretization methods in data-driven problems. We present an extension of the classical sparse grid approach that allows us to tackle high-dimensional problems by spatially adaptive refinement, modified ansatz functions, and efficient regularization techniques. The competitiveness of this method is shown for typical benchmark problems with up to 166 dimensions for classification in data mining, pointing out properties of sparse grids in this context. To gain insight into the adaptive refinement and to examine the scope for further improvements, the approximation of non-smooth indicator functions with adaptive sparse grids has been studied as a model problem. As an example for an improved adaptive grid refinement, we present results for an edge-detection strategy.