This article describes an image object classification method based on a definite positive graph kernel inducing a metric between graphs. This kernel may thus be ...
Jan 14, 2020 · This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years.
Automatic object recognition plays a central role in nu- merous applications, such as image retrieval and robot navigation. A now classical strategy ...
This article describes an image object classification method based on a definite positive graph kernel inducing a metric between graphs. This kernel may thus be ...
In this paper, we apply kernel methods to graph clas- sification problems. To achieve the goal, we have to de- sign an appropriate kernel for computing ...
Graph kernels provide thus an implicit graph embedding hence allowing to combine structural and statistical pattern recognition fields. We propose in this paper ...
Dec 27, 2024 · In node classification, graph kernel measures the similarity between nodes in the graph rather than the entire graph. GNNs also learn nodes in ...
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Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of ...
Mar 12, 2020 · Typically, a kernel matrix is computed upon applying a graph kernel. This matrix is passed to a kernel-based machine algorithm like Support ...
A kernel matrix, called graph Laplacian kernel, is then defined based on the graph Laplacian. ... kernel-based classifier can be used for classification.