LEDA: a platform for combinatorial and geometric computing
LEDA: a platform for combinatorial and geometric computing
Feature Construction with Version Spaces for Biochemical Applications
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Kernel independent component analysis
The Journal of Machine Learning Research
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Learning from interpretations: a rooted kernel for ordered hypergraphs
Proceedings of the 24th international conference on Machine learning
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Graph kernels between point clouds
Proceedings of the 25th international conference on Machine learning
Direct mining of discriminative and essential frequent patterns via model-based search tree
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing Feature Sets for Structured Data
ECML '07 Proceedings of the 18th European conference on Machine Learning
G-hash: towards fast kernel-based similarity search in large graph databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Chronic Rat Toxicity Prediction of Chemical Compounds Using Kernel Machines
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Kernels for Periodic Time Series Arising in Astronomy
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Context-sensitive refinements for stochastic optimisation algorithms in inductive logic programming
Artificial Intelligence Review
Geometry aware local kernels for object recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Biomedical text categorization with concept graph representations using a controlled vocabulary
Proceedings of the 11th International Workshop on Data Mining in Bioinformatics
Text Categorization of Biomedical Data Sets Using Graph Kernels and a Controlled Vocabulary
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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We propose a new kernel function for attributed molecular graphs, which is based on the idea of computing an optimal assignment from the atoms of one molecule to those of another one, including information on neighborhood, membership to a certain structural element and other characteristics for each atom. As a byproduct this leads to a new class of kernel functions. We demonstrate how the necessary computations can be carried out efficiently. Compared to marginalized graph kernels our method in some cases leads to a significant reduction of the prediction error. Further improvement can be gained, if expert knowledge is combined with our method. We also investigate a reduced graph representation of molecules by collapsing certain structural elements, like e.g. rings, into a single node of the molecular graph.