Multilayer Perceptrons May Learn Simple Rules Quickly
نویسنده
چکیده
Zero temperature Gibbs learning is considered for a connected committee machine with K hidden units. For large K, the scale of the learning curve strongly depends on the target rule. When learning a perceptron, the sample size P needed for optimal generalization scales so that N P KN, where N is the dimension of the input. This even holds for a noisy perceptron rule if a new input is classiied by the majority vote of all students in the version space. When learning a committee machine with M hidden units, 1 M K, optimal generalization requires p MKN P.
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