Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Universal Approximation Capability of Cascade Correlation for Structures
Neural Computation
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
Robust combination of neural networks and hidden Markov models for speech recognition
IEEE Transactions on Neural Networks
Recognition of sequences of graphical patterns
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
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Supervised relational learning over labeled graphs, e.g. via recursive neural nets, received considerable attention from the connectionist community. Surprisingly, with the exception of recursive self organizing maps, unsupervised paradigms have been far less investigated. In particular, no algorithms for density estimation over graphs are found in the literature. This paper introduces first a formal notion of probability density function (pdf) over graphical spaces. It then proposes a maximum-likelihood pdf estimation technique, relying on the joint optimization of a recursive encoding network and a constrained radial basis functions-like net. Preliminary experiments on synthetically generated samples of labeled graphs are analyzed and tested statistically.