A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Fast discovery of association rules
Advances in knowledge discovery and data mining
Making large-scale support vector machine learning practical
Advances in kernel methods
Graph isomorphism testing without numerics for graphs of bounded eigenvalue multiplicity
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Robust Classification for Imprecise Environments
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Molecular feature mining in HIV data
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The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Efficiently mining frequent trees in a forest
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Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On the Number of Simple Cycles in Planar Graphs
Combinatorics, Probability and Computing
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Weighted decomposition kernels
ICML '05 Proceedings of the 22nd international conference on Machine learning
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Effective rule induction from labeled graphs
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Learning from interpretations: a rooted kernel for ordered hypergraphs
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Graph nodes clustering with the sigmoid commute-time kernel: A comparative study
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Graph kernels based on tree patterns for molecules
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A family of novel graph kernels for structural pattern recognition
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PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Cyclic pattern kernels revisited
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Similarity boosting for label noise tolerance in protein-chemical interaction prediction
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Support vector inductive logic programming
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A new protein graph model for function prediction
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Text Categorization of Biomedical Data Sets Using Graph Kernels and a Controlled Vocabulary
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With applications in biology, the world-wide web, and several other areas, mining of graph-structured objects has received significant interest recently. One of the major research directions in this field is concerned with predictive data mining in graph databases where each instance is represented by a graph. Some of the proposed approaches for this task rely on the excellent classification performance of support vector machines. To control the computational cost of these approaches, the underlying kernel functions are based on frequent patterns. In contrast to these approaches, we propose a kernel function based on a natural set of cyclic and tree patterns independent of their frequency, and discuss its computational aspects. To practically demonstrate the effectiveness of our approach, we use the popular NCI-HIV molecule dataset. Our experimental results show that cyclic pattern kernels can be computed quickly and offer predictive performance superior to recent graph kernels based on frequent patterns.