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Nonparametric small random networks for graph-structured pattern recognition


Tempo di lettura: < 1 minuto

Taking inspiration from the probabilistic principles underlying the topological regularities observed in random networks, the paper presents a simple and efficient Bayesian framework for the classification of (small) labeled random networks.

The proposed “graphical model” relies on a Parzen window estimate of the pairwise vertex–vertex probability distribution under an implicit Markov assumption. Experiments show that, in spite of its simplicity, the approach is at least as accurate as the state-of-the-art machines.

The highest average recognition accuracies to date were obtained on the friendly + unfriendly Mutagenesis classification task.

Authors: Edmondo Trentin, Ernesto Di Iorio

Book Title: Neurocomputing

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