Cavity approach to noisy learning in nonlinear perceptrons.
We analyze the learning of noisy teacher-generated examples by nonlinear and differentiable student perceptrons using the cavity method. The generic activation of an example is a function of the cavity activation of the example, which is its activation in the perceptron that learns without the example. Mean-field equations for the macroscopic parameters and the stability condition yield results consistent with the replica method. When a single value of the cavity activation maps to multiple values of the generic activation, there is a competition in learning strategy between preferentially learning an example and sacrificing it in favor of the background adjustment. We find parameter regimes in which examples are learned preferentially or sacrificially, leading to a gap in the activation distribution. Full phase diagrams of this complex system are presented, and the theory predicts the existence of a phase transition from poor to good generalization states in the system. Simulation results confirm the theoretical predictions.
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Department of Physics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
Michael Wong
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D055592:Biophysical Phenomena; D001703:Biophysics; D003071:Cognition; D006801:Humans; D007858:Learning; D015233:Models, Statistical; D009415:Nerve Net; D010465:Perception