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.
Bi-causal Recurrent Cascade Correlation
SPERDUTI, ALESSANDRO
2000
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.
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