A new approach is proposed for the integration of neural networks (NN) with machine learning techniques to build up an image classification system. In particular, the author uses a symbolic technique for inductive learning from examples to provide object models. Such models are used to design the architecture and to initialize the weights of a backpropagation NN. Models include uncertainty aspects represented by fuzzy predicates, and relational properties for contextual classification. Both aspects are suitably mapped into the automatically designed NN. Preliminary results in a biomedical application are presented.

Image classification by integration of neural networks and machine learning

SERPICO, SEBASTIANO
1991-01-01

Abstract

A new approach is proposed for the integration of neural networks (NN) with machine learning techniques to build up an image classification system. In particular, the author uses a symbolic technique for inductive learning from examples to provide object models. Such models are used to design the architecture and to initialize the weights of a backpropagation NN. Models include uncertainty aspects represented by fuzzy predicates, and relational properties for contextual classification. Both aspects are suitably mapped into the automatically designed NN. Preliminary results in a biomedical application are presented.
1991
078030033
078030033
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/843909
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