Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
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
Extensions of marginalized graph kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A survey of kernel and spectral methods for clustering
Pattern Recognition
A new protein graph model for function prediction
Computational Biology and Chemistry
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The development of learning algorithms for structured data, i.e. data that cannot be represented by numerical vectors, is a relevant challenge in machine learning. Kernel Methods, which is a leading machine learning technology for vectorial data, recently tackled the structured data. In this paper we focus our attention on Kernel Methods that face up to data that can be represented by means of graphs, by providing an in-depth review through a comprehensive approach to the research hints and the main open problems in this area of research.