33 citations · 60 across the 6 of their papers we have counts for
6 papers
Interpretable transformed ANOVA approximation on the example of the prevention of forest fires
Daniel Potts, Michael Schmischke
The distribution of data points is a key component in machine learning. In most cases, one uses min-max normalization to obtain nodes in or Z-score normalization for standa…
Interpretable Approximation of High-Dimensional Data
Daniel Potts, Michael Schmischke
In this paper we apply the previously introduced approximation method based on the ANOVA (analysis of variance) decomposition and Grouped Transformations to synthetic and real data…
Grouped Transformations and Regularization in High-Dimensional Explainable ANOVA Approximation
Felix Bartel, Daniel Potts, Michael Schmischke
In this paper we propose a tool for high-dimensional approximation based on trigonometric polynomials where we allow only low-dimensional interactions of variables. In a general hi…
Learning multivariate functions with low-dimensional structures using polynomial bases
Daniel Potts, Michael Schmischke
In this paper we propose a method for the approximation of high-dimensional functions over finite intervals with respect to complete orthonormal systems of polynomials. An importan…
Approximation of high-dimensional periodic functions with Fourier-based methods
Daniel Potts, Michael Schmischke
In this paper we propose an approximation method for high-dimensional -periodic functions based on the multivariate ANOVA decomposition. We provide an analysis on the classical…
Nonequispaced Fast Fourier Transform (NFFT) Interface for Julia
Michael Schmischke
This report describes the newly added Julia interface to the NFFT3 library. We explain the multidimensional NFFT algorithm and basics of the interface. Furthermore, we go into deta…