Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Application of Cascade Correlation Networks for Structures toChemistry
Applied Intelligence
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
Contextual processing of structured data by recursive cascade correlation
IEEE Transactions on Neural Networks
Neural network for graphs: a contextual constructive approach
IEEE Transactions on Neural Networks
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We propose a novel simple approach to deal with fairly general graph structures by neural networks. Using a constructive approach, the model Neural Network for Graphs (NN4G) exploits the contextual information stored in the hidden units progressively added to the network, without introducing cycles in the definition of the state variables. In contrast to previous neural networks for structures, NN4G is not recursive but uses standard neurons (with no feedbacks) that traverse each graph without hierarchical assumptions on its topology, allowing the extension of structured domain to cyclic directed/undirected graphs. Initial experimental results, obtained on the prediction of the boiling point of alkanes and on the classification of artificial cyclic structures, show the effectiveness of this new approach.