Add to ORKGWe derive the correspondence between regularization operators used in Regularization Networks and Hilbert Schmidt Kernels appearing in Support Vector Machines. More specifically, we prove that the Green’s Functions associated with regularization operators are suitable Support Vector Kernels with equivalent regularization properties. As a by–product we show that a large number of Radial Basis Functions namely conditionally positive definite functions may be used as Support Vector kernels.
AbstractIn nonparametric classification and regression problems, regularized kernel methods, in part...
Fourier-based regularisation is considered for the support vector machine (SVM) classification probl...
In this paper we review some basic concepts of the theory of Support Vedor Machines and derive some ...
We derive the correspondence between regularization operators used in Regularization Networks and Hi...
n this paper a correspondence is derived between regularization operators used in regularization net...
We derive new bounds for the generalization error of kernel machines, such as support vector machine...
We derive new bounds for the generalization error of kernel machines, such as support vector machine...
We derive new bounds for the generalization error of feature space machines, such as support vector ...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
When dealing with a Support Vector Machine (SVM) with a strictly positive definite kernel, a common ...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
This thesis develops the theory and practise of reproducing kernel methods. Many functional inverse ...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
We show in this brief paper the equivalence of the support vector machine and regularization neural ...
Many recently proposed learning algorithms are clearly inspired by Support Vector Machines. Some of ...
AbstractIn nonparametric classification and regression problems, regularized kernel methods, in part...
Fourier-based regularisation is considered for the support vector machine (SVM) classification probl...
In this paper we review some basic concepts of the theory of Support Vedor Machines and derive some ...
We derive the correspondence between regularization operators used in Regularization Networks and Hi...
n this paper a correspondence is derived between regularization operators used in regularization net...
We derive new bounds for the generalization error of kernel machines, such as support vector machine...
We derive new bounds for the generalization error of kernel machines, such as support vector machine...
We derive new bounds for the generalization error of feature space machines, such as support vector ...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
When dealing with a Support Vector Machine (SVM) with a strictly positive definite kernel, a common ...
In regularized kernel methods, the solution of a learning problem is found by minimizing functionals...
This thesis develops the theory and practise of reproducing kernel methods. Many functional inverse ...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
We show in this brief paper the equivalence of the support vector machine and regularization neural ...
Many recently proposed learning algorithms are clearly inspired by Support Vector Machines. Some of ...
AbstractIn nonparametric classification and regression problems, regularized kernel methods, in part...
Fourier-based regularisation is considered for the support vector machine (SVM) classification probl...
In this paper we review some basic concepts of the theory of Support Vedor Machines and derive some ...