Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Computing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Classification with sparse grids using simplicial basis functions
Intelligent Data Analysis
Globus toolkit version 4: software for service-oriented systems
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
Grid-Supported Simulation of Vapour-Liquid Equilibria with GridSFEA
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Spatially adaptive sparse grids for high-dimensional data-driven problems
Journal of Complexity
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Common techniques tackling the task of classification in data mining employ ansatz functions associated to training data points to fit the data as well as possible. Instead, the feature space can be discretized and ansatz functions centered on grid points can be used. This allows for classification algorithms scaling only linearly in the number of training data points, enabling to learn from data sets with millions of data points. As the curse of dimensionality prohibits the use of standard grids, sparse grids have to be used.Adaptive sparse grids allow to get a trade-off between both worlds by refining in rough regions of the target function rather than in smooth ones. We present new results for some typical classification tasks and show first observations of dimension adaptivity. As the study of the critical parameters during development involves many computations for different parameter values, we used a grid environment which we present.