A competitive modular connectionist architecture
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
A universal theorem on learning curves
Neural Networks
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Deterministic learning rules for Boltzmann machines
Neural Networks
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Singularities Affect Dynamics of Learning in Neuromanifolds
Neural Computation
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Theories of learning and generalization hold that the generalization bias, defined as the difference between the training error and the generalization error, increases on average with the number of adaptive parameters. This article, however, shows that this general tendency is violated for a gaussian mixture model. For temperatures just below the first symmetry breaking point, the effective number of adaptive parameters increases and the generalization bias decreases. We compute the dependence of the neural information criterion on temperature around the symmetry breaking. Our results are confirmed by numerical cross-validation experiments.