An Eigenspace Projection Clustering Method for Inexact Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with non-positive kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A spectral approach to learning structural variations in graphs
Pattern Recognition
Kernels, regularization and differential equations
Pattern Recognition
Graph Characteristic from the Gauss-Bonnet Theorem
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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
Geometric Characterizations of Graphs Using Heat Kernel Embeddings
Proceedings of the 13th IMA International Conference on Mathematics of Surfaces XIII
Discovering Emerging Graph Patterns from Chemicals
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Graph embedding using quantum commute times
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Graph embedding in vector spaces by means of prototype selection
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Graph embedding through random walk for shortest paths problems
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
The Journal of Machine Learning Research
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
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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Chemoinformatics is a well established research field concerned with the discovery of molecule's properties through informational techniques. Computer science's research fields mainly concerned by chemoinformatics are machine learning and graph theory. From this point of view, graph kernels provide a nice framework combining machine learning and graph theory techniques. Such kernels prove their efficiency on several chemoinformatics problems and this paper presents two new graph kernels applied to regression and classification problems. The first kernel is based on the notion of edit distance while the second is based on subtrees enumeration. The design of this last kernel is based on a variable selection step in order to obtain kernels defined on parsimonious sets of patterns. Performances of both kernels are investigated through experiments.