Capabilities and training of feedforward nets
Neural networks
Logo Recognition by Recursive Neural Networks
GREC '97 Selected Papers from the Second International Workshop on Graphics Recognition, Algorithms and Systems
Extended Cascade-Correlation for Syntactic and Structural Pattern Recognition
SSPR '96 Proceedings of the 6th International Workshop on Advances in Structural and Syntactical Pattern Recognition
Universal Approximation Capability of Cascade Correlation for Structures
Neural Computation
Computational capabilities of graph neural networks
IEEE Transactions on Neural Networks
A ConceptLink graph for text structure mining
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
On the implementation of frontier-to-root tree automata in recursive neural networks
IEEE Transactions on Neural Networks
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
Overview of the INEX 2009 XML mining track: clustering and classification of XML documents
INEX'09 Proceedings of the Focused retrieval and evaluation, and 8th international conference on Initiative for the evaluation of XML retrieval
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This paper introduces a novel approach for processing a general class of structured information, viz., a graph of graphs structure, in which each node of the graph can be described by another graph, and each node in this graph, in turn, can be described by yet another graph, up to a finite depth. This graph of graphs description may be used as an underlying model to describe a number of naturally and artificially occurring systems, e.g. nested hypertexted documents. The approach taken is a data driven method in that it learns from a set of examples how to classify the nodes in a graph of graphs. To the best of our knowledge, this is the first time that a machine learning approach is enabled to deal with such structured problem domains. Experimental results on a relatively large scale real world problem indicate that the learning is efficient. This paper presents some preliminary results which show that the classification performance is already close to those provided by the state-of-the-art ones.