Add to ORKGA new kernel function between two labeled graphs is presented. Feature vectors are defined as the counts of label paths produced by random walks on graphs. The kernel computation finally boils down to obtaining the stationary state of a discrete-time linear system, thus is efficiently performed by solving simultaneous linear equations. Our kernel is based on an infinite dimensional feature space, so it is fundamentally different from other string or tree kernels based on dynamic programming. We will present promising empirical results in classification of chemical compounds
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a...
Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a...
A new kernel function between two labeled graphs is presented. Feature vectors are de-fined as the c...
Positive definite kernels between labeled graphs have recently been proposed. They enable the appl...
Positive denite kernels between labeled graphs have recently been proposed. They enable the applicat...
We present a unified framework to study graph kernels, special cases of which include the random wal...
We present a unified framework to study graph kernels, special cases of which include the random wal...
We present a unified framework to study graph kernels, special cases of which include the random wal...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
We present a unified framework to study graph kernels, special cases of which include the random wa...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a...
Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a...
A new kernel function between two labeled graphs is presented. Feature vectors are de-fined as the c...
Positive definite kernels between labeled graphs have recently been proposed. They enable the appl...
Positive denite kernels between labeled graphs have recently been proposed. They enable the applicat...
We present a unified framework to study graph kernels, special cases of which include the random wal...
We present a unified framework to study graph kernels, special cases of which include the random wal...
We present a unified framework to study graph kernels, special cases of which include the random wal...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
We present a unified framework to study graph kernels, special cases of which include the random wa...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a...
Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a...