The effectiveness of connectionist models in emulating intelligent behaviour and solving significant practical problems is strictly related to the capability of the learning algorithms to find optimal or near-optimal solutions and to generalize to new examples. This paper reviews some theoretical contributions to optimal learning in the attempt to provide a unified view and give the state of the art in the field. The focus of the review is on the problem of local minima in the cost function that is likely to affect more or less any learning algorithm. Starting from this analysis, we briefly review proposals for discovering optimal solutions and suggest conditions for designing architectures tailored to a given task.
Bianchini, M., P., F., Gori, M., Maggini, M. (1998). Optimal Learning in Artificial Neural Networks. In Neural Network Systems Techniques and Applications, Volume 2: Optimization Techniques (pp. 1-51). Academic Press.
Optimal Learning in Artificial Neural Networks
BIANCHINI, MONICA;GORI, MARCO;MAGGINI, MARCO
1998-01-01
Abstract
The effectiveness of connectionist models in emulating intelligent behaviour and solving significant practical problems is strictly related to the capability of the learning algorithms to find optimal or near-optimal solutions and to generalize to new examples. This paper reviews some theoretical contributions to optimal learning in the attempt to provide a unified view and give the state of the art in the field. The focus of the review is on the problem of local minima in the cost function that is likely to affect more or less any learning algorithm. Starting from this analysis, we briefly review proposals for discovering optimal solutions and suggest conditions for designing architectures tailored to a given task.File | Dimensione | Formato | |
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https://hdl.handle.net/11365/13155
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