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Hyperparameters Evidence and Generalisation for an Unrealisable Rule
Neural Information Processing Systems
Using a statistical mechanical formalism we calculate the evidence, generalisation error and consistency measure for a linear perceptron trained and tested on a set of examples generated by a non linear teacher. The teacher is said to be unrealisable because the student can never model it without error. Our model allows us to interpolate between the known case of a linear teacher, and an unrealisable, nonlinear teacher. A comparison of the hyperparameters which maximise the evidence with thosedblp:conf/nips/MarionS94 fatcat:nl2aza4gb5ccrnwgfcbmcrmd3e