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2012
Conference Paper
Titel

A dimension adaptive combination technique using localised adaptation criteria

Abstract
We present a dimension adaptive sparse grid combination technique for the machine learning problem of regression. A function over a d-dimensional space, which assumedly describes the relationship between the features and the response variable, is reconstructed using a linear combination of partial functions; these may depend only on a subset of all features. The partial functions, which are piecewise multilinear, are adaptively chosen during the computational procedure. This approach (approximately) identifies the anova-decomposition of the underlying problem. We introduce two new localized criteria, one inspired by residual estimators based on a hierarchical subspace decomposition, for the dimension adaptive grid choice and investigate their performance on real data.
Author(s)
Garcke, J.
Hauptwerk
Modeling, simulation and optimization of complex processes
Konferenz
International Conference on High Performance Scientific Computing 2009
Thumbnail Image
DOI
10.1007/978-3-642-25707-0_10
Language
English
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Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI
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