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(PDF) Training a Sigmoidal Network is Difficult
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In this paper we show that the loading problem for a 3-node architecture with sigmoidal activation is NP-hard if the input dimension varies, ...
Training a Single Sigmoidal Neuron Is Hard
Massachusetts Institute of Technology
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由 J Šíma 著作2002被引用 76 次 — Abstract. We first present a brief survey of hardness results for training feed forward neural networks. These results are then completed by the proof.
Training a Sigmoidal Node Is Hard | Neural Computation
Massachusetts Institute of Technology
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由 DR Hush 著作1999被引用 34 次 — This article proves that the task of computing near-optimal weights for sigmoidal nodes under the L1 regression norm is NP-Hard. For the special case where ...
Training a sigmoidal network is difficult - PUB - Uni Bielefeld
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Hammer, B. (1998). Training a sigmoidal network is difficult. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks (pp. 255-260).
Training a single sigmoidal neuron is hard - ACM Digital Library
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由 J Sima 著作2002被引用 76 次 — We first present a brief survey of hardness results for training feedforward neural networks. These results are then completed by the proof that the ...
Training a Sigmoidal Node Is Hard
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It is proved that the task of computing near-optimal weights for sigmoidal nodes under the L1 regression norm is NP-Hard, and it suggests that although such ...
Minimizing the Quadratic Training Error of a Sigmoid ...
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由 J Šíma 著作2001被引用 3 次 — B. Hammer: Training a sigmoidal network is difficult. In M. Verleysen (ed.) Proceedings of the ESANN'98 Sixth European Symposium on Artificial Neural Networks ...
Training a sigmoidal node is hard - ACM Digital Library
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由 DR Hush 著作1999被引用 34 次 — Training a single sigmoidal neuron is hard. We first present a brief survey of hardness results for training feedforward neural networks. · An activation ...
Training a Single Sigmoidal Neuron Is Hard
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由 J Šíma 著作2002被引用 77 次 — We first present a brief survey of hardness results for training feedfor- ward neural networks. These results are then completed by the proof that.
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Minimizing the Quadratic Training Error of a Sigmoid ...
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由 J Šíma 著作2001被引用 3 次 — The preceding results suggest that training feedforward networks with fixed architectures is hard indeed. However, the possible way out of this situation might ...