Learning User Profiles in NAUTILUS
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Inductive Learning in Symbolic Domains Using Structure-Driven Recurrent Neural Networks
KI '96 Proceedings of the 20th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Recursive neural networks learn to localize faces
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Recursive processing of cyclic graphs
IEEE Transactions on Neural Networks
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Recursive neural networks are a powerful tool for processing structured data. According to the recursive learning paradigm, the input information consists of directed positional acyclic graphs (DPAGs). In fact, recursive networks are fed following the partial order defined by the links of the graph. Unfortunately, the hypothesis of processing DPAGs is sometimes too restrictive, being the nature of some real–world problems intrinsically cyclic. In this paper, the methodology proposed in [1,2] to process cyclic directed graphs is tested on some interesting problems in the field of structural pattern recognition. Such preliminary experimentation shows very promising results.