On the editing distance between unordered labeled trees
Information Processing Letters
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph kernels based on tree patterns for molecules
Machine Learning
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Bridging the Gap Between Graph Edit Distance and Kernel Machines
Speeding up graph edit distance computation through fast bipartite matching
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
| Hi-index | 0.00 |
Graph kernels allow to define metrics on graph space and constitute thus an efficient tool to combine advantages of structural and statistical pattern recognition fields. Within the chemoinformatics framework, kernels are usually defined by comparing the number of occurences of patterns extracted from two different graphs. Such a graph kernel construction scheme neglects the fact that similar but not identical patterns may lead to close properties. We propose in this paper to overcome this drawback by defining our kernel as a weighted sum of comparisons between all couples of patterns. In addition, we propose an efficient computation of the optimal edit distance on a limited set of finite trees. This extension has been tested on two chemoinformatics problems.