Machine Learning
Self-Organizing Maps
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
Learning Nonsparse Kernels by Self-Organizing Maps for Structured Data
IEEE Transactions on Neural Networks
Neurocomputing
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Many different paradigms have been studied in the past to treat tree structured data, including kernel and neural based approaches. However, both types of methods have their own drawbacks. Kernels typically can only cope with discrete labels and tend to be sparse. On the other side, SOM-SD, an extension of the SOM for structured data, is unsupervised and Markovian, i.e. the representation of a subtree does not consider where the subtree appears in a tree. In this paper, we present a hybrid approach which tries to overcome these problems. In particular, we propose a new kernel based on SOM-SD which adds information about the relative position of subtrees (the route) to the activation of the nodes in such a way to discriminate even those subtrees originally encoded by the same prototypes. Experiments have been performed against two well known benchmark datasets with promising results.