Bi-Causal Recurrent Cascade Correlation

Authors:
A. Micheli;D. Sona;A. Sperduti
Affiliations:
-;-;-
Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Year:
2000

Citing 0
Cited 2

Universal Approximation Capability of Cascade Correlation for Structures

Neural Computation
Formal determination of context in contextual recursive cascade correlation networks

ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing

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Abstract

Recurrent neural networks fail to deal with prediction tasks, which do not satisfy the causality assumption. We propose to exploit bi-causality to extend the Recurrent Cascade Correlation model in order to deal with contextual prediction tasks. Preliminary results on artificial data show the ability of the model to preserve the prediction capability of Recurrent Cascade Correlation on strict causal tasks, while extending this capability also to prediction tasks involving the future.