Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aiolli, F., Da San Martino, G., Hagenbuchner, M., Sperduti, A.: Learning nonsparse kernels by self organizing maps for structured data. IEEE Transactions on Neural Networks 20, 1938–1949 (2009)
Aiolli, F., Da San Martino, G., Sperduti, A.: Route kernels for trees. In: International Conference on Machine Learning, p. 3 (2009)
Aiolli, F., Da San Martino, G., Sperduti, A., Hagenbuchner, M.: Kernelized self organizing maps for structured data. In: ESANN 2007 Conference, April 24-27 (2007)
Collins, M., Duffy, N.: New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In: ACL 2002 (2002)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Denoyer, L., Gallinari, P.: Report on the xml mining track at inex 2005 and inex 2006: categorization and clustering of xml documents. SIGIR Forum 41(1), 79–90 (2007)
Gartner, T.: A survey of kernels for structured data. SIGKDD Explorations 5(1), 49–58 (2003)
Hagenbuchner, M., Sperduti, A., Tsoi, A.C.: A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks 14(3), 491–505 (2003)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)
Suzuki, J., Isozaki, H.: Sequence and tree kernels with statistical feature mining. In: Weiss, Y., Schölkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems, vol. 18, pp. 1321–1328. MIT Press, Cambridge (2006)
Trentini, F., Hagenbuchner, M., Sperduti, A., Scarselli, F.: A self-organising map approach for clustering of xml documents. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, part of the IEEE World Congress on Computational Intelligence, WCCI 2006, Vancouver, BC, Canada, July 16-21, pp. 1805–1812. IEEE Press, Los Alamitos (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aiolli, F., Da San Martino, G., Sperduti, A. (2010). A New Tree Kernel Based on SOM-SD. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-15822-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15821-6
Online ISBN: 978-3-642-15822-3
eBook Packages: Computer ScienceComputer Science (R0)