Add to ORKGThis paper gives a survey of results in the mathematical literature on positive definite kernels and their associated structures. We concentrate on properties which seem potentially relevant for Machine Learning and try to clarify some results that have been misused in the literature. Moreover we consider different lines of generalizations of positive definite kernels. Namely we deal with operator-valued kernels and present the general framework of Hilbertian subspaces of Schwartz which we use to introduce kernels which are distributions. Finally indefinite kernels and their associated reproducing kernel spaces are considered
Abstract. We discuss the structure of positive definite kernels in terms of operator models. In part...
Kernel-based methods and their underlying structure of reproducing kernel Hilbert spaces (RKHS) are ...
We analyze reproducing kernel Hilbert spaces of positive definite kernels on a topological space X b...
This paper gives a survey of results in the mathematical literature on positive definite kernels and...
The correspondence between reproducing kernel Hilbert spaces and positive definite kernels is well u...
Conditionally positive definite kernels provide a powerful tool for scattered data approximation. Ma...
Positive definite kernels and their generalizations, as, e.g., the conditionally positive definite k...
We review machine learning methods employing positive definite kernels. These methods formulate lear...
We review machine learning methods employing positive definite kernels. These methods formulate lear...
We review machine learning methods employing positive definite kernels. These methods formulate lear...
AbstractThis paper deals with conditionally positive definite kernels on Euclidean spaces. The focus...
AbstractConditionally positive definite kernels are frequently used in multi-dimensional data fittin...
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on proba...
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probabi...
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probabi...
Abstract. We discuss the structure of positive definite kernels in terms of operator models. In part...
Kernel-based methods and their underlying structure of reproducing kernel Hilbert spaces (RKHS) are ...
We analyze reproducing kernel Hilbert spaces of positive definite kernels on a topological space X b...
This paper gives a survey of results in the mathematical literature on positive definite kernels and...
The correspondence between reproducing kernel Hilbert spaces and positive definite kernels is well u...
Conditionally positive definite kernels provide a powerful tool for scattered data approximation. Ma...
Positive definite kernels and their generalizations, as, e.g., the conditionally positive definite k...
We review machine learning methods employing positive definite kernels. These methods formulate lear...
We review machine learning methods employing positive definite kernels. These methods formulate lear...
We review machine learning methods employing positive definite kernels. These methods formulate lear...
AbstractThis paper deals with conditionally positive definite kernels on Euclidean spaces. The focus...
AbstractConditionally positive definite kernels are frequently used in multi-dimensional data fittin...
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on proba...
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probabi...
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probabi...
Abstract. We discuss the structure of positive definite kernels in terms of operator models. In part...
Kernel-based methods and their underlying structure of reproducing kernel Hilbert spaces (RKHS) are ...
We analyze reproducing kernel Hilbert spaces of positive definite kernels on a topological space X b...