The nature of statistical learning theory
The nature of statistical learning theory
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph distances using graph union
Pattern Recognition Letters
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernels and Distances for Structured Data
Machine Learning
On Classifier Domains of Competence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Graph edit distance with node splitting and merging, and its application to diatom identification
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Image classification using marginalized kernels for graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Kernel fusion for image classification using fuzzy structural information
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
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In this paper we propose the use of a simple kernel function based on the graph edit distance. The kernel function allows us to apply a wide range of statistical algorithms to the problem of attributed graph matching. The function we describe is simple to compute and leads to several convenient interpretations of geometric properties of graphs in their implicit vector space representation. Although the function is not generally positive definite, we show in experiments on real-world data that the kernel approach may result in a significant improvement of the graph matching and classification performance using support vector machines and kernel principal component analysis.