We introduce a novel constructive algorithm which progressively builds the architecture of GraphESN, which generalizes Reservoir Computing to learning in graph domains. Exploiting output feedback signals in a forward fashion in such construction, allows us to introduce supervision in the reservoir encoding process. The potentiality of the proposed approach is experimentally assessed on real-world tasks from Toxicology.
Constructive Reservoir Computation with Output Feedbacks for Structured Domains
GALLICCHIO, CLAUDIO;MICHELI, ALESSIO;
2012-01-01
Abstract
We introduce a novel constructive algorithm which progressively builds the architecture of GraphESN, which generalizes Reservoir Computing to learning in graph domains. Exploiting output feedback signals in a forward fashion in such construction, allows us to introduce supervision in the reservoir encoding process. The potentiality of the proposed approach is experimentally assessed on real-world tasks from Toxicology.File in questo prodotto:
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