Many researchers are quite skeptical about the actual behavior of neural network learning algorithms like backpropagation. One of the major problems is with the lack of clear theoretical results on optimal convergence, particularly for pattern mode algorithms. In this paper, we prove the companion of Rosenblatt's PC (perceptron convergence) theorem for feedforward networks (1960), stating that pattern mode backpropagation converges to an optimal solution for linearly separable patterns.
Gori, M., Maggini, M. (1996). Optimal convergence of on-line backpropagation. IEEE TRANSACTIONS ON NEURAL NETWORKS, 7(1), 251-254 [10.1109/72.478415].
Optimal convergence of on-line backpropagation
GORI, MARCO;MAGGINI, MARCO
1996-01-01
Abstract
Many researchers are quite skeptical about the actual behavior of neural network learning algorithms like backpropagation. One of the major problems is with the lack of clear theoretical results on optimal convergence, particularly for pattern mode algorithms. In this paper, we prove the companion of Rosenblatt's PC (perceptron convergence) theorem for feedforward networks (1960), stating that pattern mode backpropagation converges to an optimal solution for linearly separable patterns.
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https://hdl.handle.net/11365/7633
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