We propose a general framework for unsupervised recurrent and recursive networks. This proposal covers various popular approaches like standard self organizing maps (SOM), temporal Kohonen maps, recursive SOM, and SOM for structured data. We define Hebbian learning within this general framework. We show how approaches based on an energy function, like neural gas, can be transferred to this abstract framework so that proposals for new learning algorithms emerge.

A general framework for unsupervised processing of structured data

SPERDUTI, ALESSANDRO
2002

Abstract

We propose a general framework for unsupervised recurrent and recursive networks. This proposal covers various popular approaches like standard self organizing maps (SOM), temporal Kohonen maps, recursive SOM, and SOM for structured data. We define Hebbian learning within this general framework. We show how approaches based on an energy function, like neural gas, can be transferred to this abstract framework so that proposals for new learning algorithms emerge.
2002
ESANN 2002, 10th Eurorean Symposium on Artificial Neural Networks, Bruges, Belgium, April 24-26, 2002, Proceedings
2930307021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/1369516
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