منابع مشابه
Adaptive Sparse Grid Classification Using Grid Environments
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 da...
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It was shown in [1] that the task of classification in data mining can be tackled by employing ansatz functions associated to grid points in the (often high dimensional) feature-space rather than using data-centered ansatz functions. To cope with the curse of dimensionality, sparse grids have been used. The problem obtained by regularisation was solved using the combination technique for sparse...
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A real valued, deterministic and stationary time series can be embedded in a — sometimes high-dimensional — real vector space. This leads to a one-to-one relationship between the embedded, time dependent vectors in R d and the states of the underlying, unknown dynamical system that determines the time series. The embedded data points are located on an m-dimensional manifold (or even fractal) ca...
متن کاملLocal and Dimension Adaptive Sparse Grid Interpolation and Quadrature
In this paper we present a locally and dimension-adaptive sparse grid method for interpolation and integration of high-dimensional functions with discontinuities. The proposed algorithm combines the strengths of the generalised sparse grid algorithm and hierarchical surplus-guided local adaptivity. A high-degree basis is used to obtain a high-order method which, given sufficient smoothness, per...
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ژورنال
عنوان ژورنال: Journal of Computational and Applied Mathematics
سال: 2018
ISSN: 0377-0427
DOI: 10.1016/j.cam.2018.04.006