Nonmonotonic generalization bias of Gaussian mixture models.
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.
Information Science Division, Ibaraki, Japan.
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Akaho
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HJ
Kappen
D003198:Computer Simulation; D007858:Learning; D016571:Neural Networks, Computer; D016011:Normal Distribution; D013696:Temperature