Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Protein function prediction via graph kernels
Bioinformatics
Weighted decomposition kernels
ICML '05 Proceedings of the 22nd international conference on Machine learning
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
An Introduction to Chemoinformatics
An Introduction to Chemoinformatics
IEEE Computational Intelligence Magazine
Two new graph kernels and applications to chemoinformatics
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
People re-identification by graph kernels methods
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
A graph-kernel method for re-identification
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Inexact graph matching based on kernels for object retrieval in image databases
Image and Vision Computing
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
Effective graph classification based on topological and label attributes
Statistical Analysis and Data Mining
Biomedical text categorization with concept graph representations using a controlled vocabulary
Proceedings of the 11th International Workshop on Data Mining in Bioinformatics
Two new graphs kernels in chemoinformatics
Pattern Recognition Letters
Efficient graph kernels by randomization
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Graph kernels: crossing information from different patterns using graph edit distance
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Shape similarity based on a treelet kernel with edition
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Subtree selection in kernels for graph classification
International Journal of Data Mining and Bioinformatics
Text Categorization of Biomedical Data Sets Using Graph Kernels and a Controlled Vocabulary
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Optimized dissimilarity space embedding for labeled graphs
Information Sciences: an International Journal
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Motivated by chemical applications, we revisit and extend a family of positive definite kernels for graphs based on the detection of common subtrees, initially proposed by Ramon and Gärtner (Proceedings of the first international workshop on mining graphs, trees and sequences, pp. 65---74, 2003). We propose new kernels with a parameter to control the complexity of the subtrees used as features to represent the graphs. This parameter allows to smoothly interpolate between classical graph kernels based on the count of common walks, on the one hand, and kernels that emphasize the detection of large common subtrees, on the other hand. We also propose two modular extensions to this formulation. The first extension increases the number of subtrees that define the feature space, and the second one removes noisy features from the graph representations. We validate experimentally these new kernels on problems of toxicity and anti-cancer activity prediction for small molecules with support vector machines.