Sparse grids, as studied by Zenger and Griebel in the last 10 years have been very successful in the solution of partial differential equations, integral equations and classification problems. Adaptive sparse grid functions are elements of a function space lattice. Such lattices allow the generalisation of sparse grid techniques to the fitting of very high-dimensional functions with categorical and continuous variables. We have observed in first tests that these general adaptive sparse grids allow the identification of the ANOVA structure and thus provide comprehensible models. This is very important for data mining applications. Perhaps the main advantage of these models is that they do not include any spurious interaction terms and thus c...
For low-dimensional data sets with a large amount of data points, standard kernel methods are usuall...
We present a exible and scalable method for computing global solutions of high-dimensional stochasti...
Abstract — In this paper we describe methods to approximate functions and dif-ferential operators on...
Sparse grids, as studied by Zenger and Griebel in the last 10 years have been very successful in the...
We introduce a dimension adaptive sparse grid combination technique for the machine learning problem...
AbstractSparse grids allow one to employ grid-based discretization methods in data-driven problems. ...
In this paper we describe methods to approximate functions and differential operators on adaptive sp...
We present a dimension adaptive sparse grid combination technique for the machine learning problem o...
Sparse Grids (SG), due to Zenger, are the basis for efficient high dimensional approximation and hav...
Adaptive dynamical systems based on finite impulse response (FIR) models constitute an important par...
Generative dimensionality reduction methods play an important role in machine learning applications ...
When working on multidimensional problems, the number of points needed when using a tensor product g...
Summary. In this paper we present a locally and dimension-adaptive sparse grid method for interpolat...
Sparse grids have become an important tool to reduce the number of degrees of freedom of discretizat...
This volume of LNCSE is a collection of the papers from the proceedings of the third workshop on spa...
For low-dimensional data sets with a large amount of data points, standard kernel methods are usuall...
We present a exible and scalable method for computing global solutions of high-dimensional stochasti...
Abstract — In this paper we describe methods to approximate functions and dif-ferential operators on...
Sparse grids, as studied by Zenger and Griebel in the last 10 years have been very successful in the...
We introduce a dimension adaptive sparse grid combination technique for the machine learning problem...
AbstractSparse grids allow one to employ grid-based discretization methods in data-driven problems. ...
In this paper we describe methods to approximate functions and differential operators on adaptive sp...
We present a dimension adaptive sparse grid combination technique for the machine learning problem o...
Sparse Grids (SG), due to Zenger, are the basis for efficient high dimensional approximation and hav...
Adaptive dynamical systems based on finite impulse response (FIR) models constitute an important par...
Generative dimensionality reduction methods play an important role in machine learning applications ...
When working on multidimensional problems, the number of points needed when using a tensor product g...
Summary. In this paper we present a locally and dimension-adaptive sparse grid method for interpolat...
Sparse grids have become an important tool to reduce the number of degrees of freedom of discretizat...
This volume of LNCSE is a collection of the papers from the proceedings of the third workshop on spa...
For low-dimensional data sets with a large amount of data points, standard kernel methods are usuall...
We present a exible and scalable method for computing global solutions of high-dimensional stochasti...
Abstract — In this paper we describe methods to approximate functions and dif-ferential operators on...