In this work we propose a novel methodology for graph-based semi-supervised learning which is composed of two main steps: Step 1) a novel strategy for PU learning specific for Hopfield networks, which can be applied both to structured classes and to hierarchy-less contexts; Step 2) a semi-supervised classifier based on a family of parametric Hopfield networks, which embeds the negative selection performed at Step 1) in the dynamics of network.
Selection of negatives in Hopfield networks / M. Frasca, D. Malchiodi. ((Intervento presentato al convegno International Workshop on Dynamics of Multi-Level Systems (DYMULT) tenutosi a Dresden nel 2015.
Selection of negatives in Hopfield networks
M. Frasca;D. Malchiodi
2015
Abstract
In this work we propose a novel methodology for graph-based semi-supervised learning which is composed of two main steps: Step 1) a novel strategy for PU learning specific for Hopfield networks, which can be applied both to structured classes and to hierarchy-less contexts; Step 2) a semi-supervised classifier based on a family of parametric Hopfield networks, which embeds the negative selection performed at Step 1) in the dynamics of network.Pubblicazioni consigliate
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