The nature of statistical learning theory
The nature of statistical learning theory
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Central Clustering of Attributed Graphs
Machine Learning
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Improving Graph Classification by Isomap
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
A family of novel graph kernels for structural pattern recognition
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Network ensemble clustering using latent roles
Advances in Data Analysis and Classification
A random walk kernel derived from graph edit distance
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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We apply support vector learning to attributed graphs where the kernel matrices are based on approximations of the Schur-Hadamard inner product. The evaluation of the Schur-Hadamard inner product for a pair of graphs requires the determination of an optimal match between their nodes and edges. It is therefore efficiently approximated by means of recurrent neural networks. The optimal mapping involved allows a direct understanding of the similarity or dissimilarity of the two graphs considered. We present and discuss experimental results of different classifiers constructed by a SVM operating on positive semi-definite (psd) and non-psd kernel matrices.