Abstract
The Labeling RAAM model is a neural network able to encode labeled graphs in fixed size representations. In order to speed up the training procedure and for reducing in size the developed compressed representations, we propose a modular Labeling RAAM. In order to develop the modular system, we face two main problems: the mapping problem, i.e., how to map components of the structures into modules; and the membership problem, i.e., discovering which module must be used for decoding a compressed representation. The mapping between components and modules can be decided on the basis of the strongly connected components of the structures. The membership problem is solved by resorting to the BAMs derived from each LRAAM module. Preliminary results on the modular system are encouraging.
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© 1995 Springer-Verlag/Wien
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Sperduti, A., Starita, A. (1995). Modular Labeling RAAM. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_96
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_96
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
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