We give a short survey of a general discretization method based on the theory of reproducing kernels. We believe our method will become the next generation method for solving analytical problems by computers. For example, for solving linear PDEs with general boundary or initial value conditions, independently of the domains. Furthermore, we give an ultimate sampling formula and a realization of reproducing kernel Hilbert spaces
Kernel function, which allows the formulation of nonlinear variants of any algorithm that can be cas...
In this paper, we propose a method to explicitly construct a reproducing kernel Hilbert space (RKHS)...
Kernel machines traditionally arise from an elegant formulation based on measuring the smoothness of...
We found a very general discretization method for solving wide classes of mathematical problems by a...
This report is concerned with the theory of reproducing kernels. First, a background of elementary f...
This book provides a large extension of the general theory of reproducing kernels published by N. Ar...
This paper is concerned with a technique for solving a class of nonlinear systems of partial differe...
The use of operator-valued reproducing kernels is introduced in order to solve Cauchy problems, ∂N/∂...
This is a series of lectures we have held during the academic year 2004-2005 at the Department of ...
We investigate the effectiveness of reproducing kernel method (RKM) in solving partial differential ...
We investigate the effectiveness of reproducing kernel method (RKM) in solving partial differential ...
We do a preliminary study of the reproducing kernel Hilbert space having as kernel $k^d$, where $d$ ...
Many common machine learning methods such as Support Vector Machines or Gaussian process inference m...
Abstract In this paper, a new implementation of the reproducing kernel method is prop...
In this paper, convergence rate of the reproducing kernel method for solving boundary value problems...
Kernel function, which allows the formulation of nonlinear variants of any algorithm that can be cas...
In this paper, we propose a method to explicitly construct a reproducing kernel Hilbert space (RKHS)...
Kernel machines traditionally arise from an elegant formulation based on measuring the smoothness of...
We found a very general discretization method for solving wide classes of mathematical problems by a...
This report is concerned with the theory of reproducing kernels. First, a background of elementary f...
This book provides a large extension of the general theory of reproducing kernels published by N. Ar...
This paper is concerned with a technique for solving a class of nonlinear systems of partial differe...
The use of operator-valued reproducing kernels is introduced in order to solve Cauchy problems, ∂N/∂...
This is a series of lectures we have held during the academic year 2004-2005 at the Department of ...
We investigate the effectiveness of reproducing kernel method (RKM) in solving partial differential ...
We investigate the effectiveness of reproducing kernel method (RKM) in solving partial differential ...
We do a preliminary study of the reproducing kernel Hilbert space having as kernel $k^d$, where $d$ ...
Many common machine learning methods such as Support Vector Machines or Gaussian process inference m...
Abstract In this paper, a new implementation of the reproducing kernel method is prop...
In this paper, convergence rate of the reproducing kernel method for solving boundary value problems...
Kernel function, which allows the formulation of nonlinear variants of any algorithm that can be cas...
In this paper, we propose a method to explicitly construct a reproducing kernel Hilbert space (RKHS)...
Kernel machines traditionally arise from an elegant formulation based on measuring the smoothness of...